Matthew A Kupinski
 Professor, Optical Sciences
 Professor, Radiology
 Professor, BIO5 Institute
 Professor, Applied Mathematics  GIDP
 Member of the Graduate Faculty
 (520) 6212967
 Meinel Optical Sciences, Rm. 435
 Tucson, AZ 85721
 kupinski@arizona.edu
Biography
Matthew A. Kupinski is a Professor at The University of Arizona with appointments in the College of Optical Sciences, the Department of Medical Imaging, and the program in Applied Mathematics. He performs theoretical research in the field of image science. His recent research emphasis is on quantifying the quality of multimodality and adaptive medical imaging systems using objective, taskbased measures of image quality. He has a BS in physics from Trinity University in San Antonio, Texas, and received his PhD in 2000 from the University of Chicago. He is the recipient of the 2007 Mark Tetalman Award given out by the Society of Nuclear Medicine and is a member of the OSA and SPIE. Contact him at College of Optical Sciences, The University of Arizona, 1630 E. University Blvd., Tucson, Arizona 85721; mkupinski@optics.arizona.edu
Degrees
 Ph.D. Medical Physics
 University of Chicago, Chicago, Illinois, USA
 Computerized pattern classification in medical imaging
 B.S. Physics
 Trinity University, San Antonio, Texas, USA
Work Experience
 College of Optical Sciences, University of Arizona (2014  Ongoing)
 College of Optical Sciences, University of Arizona (2008  2014)
 College of Optical Sciences, University of Arizona (2002  2008)
 University of Arizona, Tucson, Arizona (2000  2002)
 University of Chicago, Chicago, Illinois (1997  1998)
 University of Chicago, Chicago, Illinois (1995  2000)
 Honeywell (1990  1993)
Licensure & Certification
 Q Clearance, Department of Energy (2013)
Interests
Research
Medical imaging, taskbased assessment of image quality, statistical decision theory, homeland security, CT imaging, SPECT, PET, MRI, information theory
Teaching
Probability and statistics, mathematical modeling, mathematical methods, statistical optics, statistical decision theory
Courses
202425 Courses

Dissertation
OPTI 920 (Fall 2024) 
Mathematical Optics Lab
OPTI 512L (Fall 2024) 
Noise In Imaging Systems
OPTI 636 (Fall 2024)
202324 Courses

Master's Report
OPTI 909 (Summer I 2024) 
Dissertation
OPTI 920 (Spring 2024) 
Probability+Stat Optics
OPTI 508 (Spring 2024) 
Dissertation
OPTI 920 (Fall 2023) 
Mathematical Optics Lab
OPTI 512L (Fall 2023) 
Noise In Imaging Systems
OPTI 636 (Fall 2023)
202223 Courses

Dissertation
OPTI 920 (Spring 2023) 
Probability+Stat Optics
OPTI 508 (Spring 2023) 
Thesis
OPTI 910 (Spring 2023) 
Dissertation
MATH 920 (Fall 2022) 
Dissertation
OPTI 920 (Fall 2022) 
Mathematical Optics Lab
OPTI 512L (Fall 2022) 
Noise In Imaging Systems
OPTI 636 (Fall 2022) 
Research
MATH 900 (Fall 2022) 
Thesis
OPTI 910 (Fall 2022)
202122 Courses

Dissertation
OPTI 920 (Spring 2022) 
Probability+Stat Optics
OPTI 508 (Spring 2022) 
Thesis
OPTI 910 (Spring 2022) 
Dissertation
OPTI 920 (Fall 2021) 
Mathematical Optics Lab
OPTI 512L (Fall 2021) 
Noise In Imaging Systems
OPTI 636 (Fall 2021) 
Thesis
OPTI 910 (Fall 2021)
202021 Courses

Dissertation
MATH 920 (Spring 2021) 
Dissertation
OPTI 920 (Spring 2021) 
Probability+Stat Optics
OPTI 508 (Spring 2021) 
Thesis
OPTI 910 (Spring 2021) 
Directed Graduate Research
OPTI 792 (Fall 2020) 
Dissertation
MATH 920 (Fall 2020) 
Dissertation
OPTI 920 (Fall 2020) 
Mathematical Optics Lab
OPTI 512L (Fall 2020) 
Noise In Imaging Systems
OPTI 636 (Fall 2020)
201920 Courses

Dissertation
MATH 920 (Spring 2020) 
Dissertation
OPTI 920 (Spring 2020) 
Probability+Stat Optics
OPTI 508 (Spring 2020) 
Thesis
OPTI 910 (Spring 2020) 
Dissertation
OPTI 920 (Fall 2019) 
Mathematical Optics Lab
OPTI 512L (Fall 2019) 
Noise In Imaging Systems
OPTI 636 (Fall 2019) 
Thesis
OPTI 910 (Fall 2019)
201819 Courses

Dissertation
OPTI 920 (Spring 2019) 
Probability+Stat Optics
OPTI 508 (Spring 2019) 
Thesis
OPTI 910 (Spring 2019) 
Mathematical Optics Lab
OPTI 512L (Fall 2018) 
Noise In Imaging Systems
OPTI 636 (Fall 2018)
201718 Courses

Directed Graduate Research
OPTI 792 (Spring 2018) 
Dissertation
OPTI 920 (Spring 2018) 
Probability+Stat Optics
OPTI 508 (Spring 2018) 
Dissertation
OPTI 920 (Fall 2017)
201617 Courses

Dissertation
OPTI 920 (Spring 2017) 
Master's Report
OPTI 909 (Spring 2017) 
Probability+Stat Optics
OPTI 508 (Spring 2017) 
Dissertation
OPTI 920 (Fall 2016) 
Independent Study
OPTI 599 (Fall 2016) 
Master's Report
OPTI 909 (Fall 2016) 
Mathematical Optics Lab
OPTI 512L (Fall 2016) 
Noise In Imaging Systems
OPTI 636 (Fall 2016)
201516 Courses

Dissertation
OPTI 920 (Spring 2016) 
Probability+Stat Optics
OPTI 508 (Spring 2016)
Scholarly Contributions
Books
 MelloThoms, C. R., & Kupinski, M. A. (2014). Image Perception, Observer Performance, and Technology Assessment. SPIE.
 Barrett, H. H., & Kupinski, M. A. (2005). SmallAnimal SPECT Imaging. Springer Science+ Business Media, Incorporated.
 Kupinski, M. A., & Barrett, H. H. (2005). Smallanimal SPECT imaging. Springer.
Chapters
 Kupinski, M. A. (2012). Evaluation and Image Quality in RadiationBased Medical Imaging. In Handbook of Particle Detection and Imaging(pp 10831093). Springer.
 Kupinski, M. A., & Clarkson, E. (2005). Objective Assessment of Image Quality. In SmallAnimal Spect Imaging(pp 101114). Springer.
 Park, S., Kupinski, M. A., Clarkson, E., & Barrett, H. H. (2003). Idealobserver performance under signal and background uncertainty. In Information Processing in Medical Imaging(pp 342353). Springer.
 Hoppin, J., Kupinski, M., Kastis, G., Clarkson, E., & Barrett, H. H. (2001). Objective comparison of quantitative imaging modalities without the use of a gold standard. In Information Processing in Medical Imaging(pp 1223). Springer.
Journals/Publications
 Nishikawa, R. M., Deserno, T. M., Madabhushi, A., Krupinski, E. A., Summers, R. M., Hoeschen, C., MelloThoms, C., Myers, K. J., Kupinski, M. A., & Siewerdsen, J. H. (2022). Fifty years of SPIE Medical Imaging proceedings papers. Journal of Medical Imaging, 9(Suppl 1).
 Rahman, A., Zhu, Y., Clarkson, E., Kupinski, M. A., Frey, E. C., & Jha, A. K. (2020). Fisher information analysis of listmode SPECT emission data for joint estimation of activity and attenuation distribution. Inverse problems, 36(8).More infoThe potential to perform attenuation and scatter compensation (ASC) in singlephoton emission computed tomography (SPECT) imaging without a separate transmission scan is highly significant. In this context, attenuation in SPECT is primarily due to Compton scattering, where the probability of Compton scatter is proportional to the attenuation coefficient of the tissue and the energy of the scattered photon and the scattering angle are related. Based on this premise, we investigated whether the SPECT scatteredphoton data acquired in listmode (LM) format and including the energy information can be used to estimate the attenuation map. For this purpose, we propose a Fisherinformationbased method that yields the CramerRao bound (CRB) for the task of jointly estimating the activity and attenuation distribution using only the SPECT emission data. In the process, a pathbased formalism to process the LM SPECT emission data, including the scatteredphoton data, is proposed. The Fisher information method was implemented on NVIDIA graphics processing units (GPU) for acceleration. The method was applied to analyze the information content of SPECT LM emission data, which contains up to firstorder scattered events, in a simulated SPECT system with parameters modeling a clinical system using realistic computational studies with 2D digital synthetic and anthropomorphic phantoms. The method was also applied to LM data containing up to secondorder scatter for a synthetic phantom. Experiments with anthropomorphic phantoms simulated myocardial perfusion and dopamine transporter (DaT)Scan SPECT studies. The results show that the CRB obtained for the attenuation and activity coefficients was typically much lower than the true value of these coefficients. An increase in the number of detected photons yielded lower CRB for both the attenuation and activity coefficients. Further, we observed that systems with better energy resolution yielded a lower CRB for the attenuation coefficient. Overall, the results provide evidence that LM SPECT emission data, including the scattered photons, contains information to jointly estimate the activity and attenuation coefficients.
 Chen, Y., Lou, Y., Wang, K., Kupinski, M. A., & Anastasio, M. A. (2019). ReconstructionAware Imaging System Ranking by Use of a SparsityDriven Numerical Observer Enabled by Variational Bayesian Inference. IEEE transactions on medical imaging, 38(5), 12511262.More infoIt is widely accepted that optimization of imaging system performance should be guided by taskbased measures of image quality. It has been advocated that imaging hardware or dataacquisition designs should be optimized by use of an ideal observer that exploits full statistical knowledge of the measurement noise and class of objects to be imaged, without consideration of the reconstruction method. In practice, accurate and tractable models of the complete object statistics are often difficult to determine. Moreover, in imaging systems that employ compressive sensing concepts, imaging hardware and sparse image reconstruction are innately coupled technologies. In this paper, a sparsitydriven observer (SDO) that can be employed to optimize hardware by use of a stochastic object model describing object sparsity is described and investigated. The SDO and sparse reconstruction method can, therefore, be "matched" in the sense that they both utilize the same statistical information regarding the class of objects to be imaged. To efficiently compute the SDO test statistic, computational tools developed recently for variational Bayesian inference with sparse linear models are adopted. The use of the SDO to rank dataacquisition designs in a stylized example as motivated by magnetic resonance imaging is demonstrated. This paper reveals that the SDO can produce rankings that are consistent with visual assessments of the reconstructed images but different from those produced by use of the traditionally employed Hotelling observer.
 Ding, Y., Barrett, H. H., Kupinski, M. A., Vinogradskiy, Y., Miften, M., & Jones, B. L. (2019). Objective assessment of the effects of tumor motion in radiation therapy. Medical physics, 46(7), 33113323.More infoInternal organ motion reduces the accuracy and efficacy of radiation therapy. However, there is a lack of tools to objectively (based on a medical or scientific task) assess the dosimetric consequences of motion, especially on an individual basis. We propose to use therapy operating characteristic (TOC) analysis to quantify the effects of motion on treatment efficacy for individual patients. We demonstrate the application of this tool with pancreatic stereotactic body radiation therapy (SBRT) clinical data and explore the origin of motion sensitivity.
 Kupinski, M. A. (2016). Development of an Ideal Observer that Incorporates Nuisance Parameters and Processes ListMode Data. JOSA A.More infoObserver models were developed to process data in listmode format in orderto perform binary discrimination tasks for use in an armscontroltreatycontext. Data used in this study was generated using GEANT4 Monte Carlosimulations for photons using custom models of plutonium inspection objects anda radiation imaging system. Observer model performance was evaluated andpresented using the area under the receiver operating characteristic curve. Theideal observer was studied under both signalknownexactly conditions and inthe presence of unknowns such as object orientation and absolute countratevariability; when these additional sources of randomness were present, theirincorporation into the observer yielded superior performance.[Journal_ref: ]
 Kupinski, M. A. (2016). Linear Models to Perform Treaty Verification Tasks for Enhanced Information Security. Nuclear Instruments and Methods in Physics Research Section A.
 MacGahan, C. J., Kupinski, M. A., Hilton, N. R., Brubaker, E. M., & Johnson, W. C. (2016). Development of an ideal observer that incorporates nuisance parameters and processes listmode data. Journal of the Optical Society of America. A, Optics, image science, and vision, 33(4), 68997.More infoObserver models were developed to process data in listmode format in order to perform binary discrimination tasks for use in an armscontroltreaty context. Data used in this study was generated using GEANT4 Monte Carlo simulations for photons using custom models of plutonium inspection objects and a radiation imaging system. Observer model performance was evaluated and presented using the area under the receiver operating characteristic curve. The ideal observer was studied under both signalknownexactly conditions and in the presence of unknowns such as object orientation and absolute countrate variability; when these additional sources of randomness were present, their incorporation into the observer yielded superior performance.
 Tseng, H. W., Fan, J., & Kupinski, M. A. (2016). Design of a practical modelobserverbased image quality assessment method for xray computed tomography imaging systems. Journal of medical imaging (Bellingham, Wash.), 3(3), 035503.More infoThe use of a channelization mechanism on model observers not only makes mimicking human visual behavior possible, but also reduces the amount of image data needed to estimate the model observer parameters. The channelized Hotelling observer (CHO) and channelized scanning linear observer (CSLO) have recently been used to assess CT image quality for detection tasks and combined detection/estimation tasks, respectively. Although the use of channels substantially reduces the amount of data required to compute image quality, the number of scans required for CT imaging is still not practical for routine use. It is our desire to further reduce the number of scans required to make CHO or CSLO an image quality tool for routine and frequent system validations and evaluations. This work explores different datareduction schemes and designs an approach that requires only a few CT scans. Three different kinds of approaches are included in this study: a conventional CHO/CSLO technique with a large sample size, a conventional CHO/CSLO technique with fewer samples, and an approach that we will show requires fewer samples to mimic conventional performance with a large sample size. The mean value and standard deviation of areas under ROC/EROC curve were estimated using the wellvalidated shuffle approach. The results indicate that an 80% data reduction can be achieved without loss of accuracy. This substantial data reduction is a step toward a practical tool for routinetaskbased QA/QC CT system assessment.
 Barrett, H. H., Myers, K. J., Hoeschen, C., Kupinski, M. A., & Little, M. P. (2015). Taskbased measures of image quality and their relation to radiation dose and patient risk. Physics in medicine and biology, 60, R1.
 Huang, J., Tankam, P., Aquavella, J. V., Hindman, H. B., Clarkson, E., Kupinski, M., & RollandThompson, J. (2015). TEAR FILM THICKNESS ESTIMATION USING OPTICAL COHERENCE TOMOGRAPHY AND MAXIMUMLIKELIHOOD ESTIMATION. Investigative Ophthalmology \& Visual Science, 56, 351351.
 Jha, A., Barrett, H. H., Frey, E. C., Clarkson, E. W., Caucci, L., & Kupinski, M. A. (2015). Singular value decomposition for photonprocessing nuclear imaging systems and applications for reconstruction and computing null functions. Physics in Medicine and Biology, 6(18), 73597385.
 Stephen, R. M., Jha, A. K., Roe, D. J., Trouard, T. P., Galons, J., Kupinski, M. A., Frey, G., Cui, H., Squire, S., Pagel, M. D., Rodriguez, J. J., Gillies, R. J., & Stopeck, A. T. (2015). Diffusion MRI with SemiAutomated Segmentation Can Serve as a Restricted Predictive Biomarker of the Therapeutic Response of Liver Metastasis. Magnetic Resonance Imaging, 33(10), 126773.
 Stephen, R. M., Roe, D. J., Jha, A. K., Trouard, T. P., Galons, J., Kupinski, M. A., Frey, G., Cui, H., Squire, S., Pagel, M. D., Rodriguez, J. J., Gillies, R. J., & Stopeck, A. T. (2015). Diffusion MRI with SemiAutomaDiffusion MRI with SemiAutomated Segmentation Can Serve as a Restricted Predictive Biomarker of the Therapeutic Response of Liver Metastasis. Magnetic Resonance Imaging.
 Stephen, R. M., Roe, D. J., Jha, A. K., Trouard, T. P., Galons, J., Kupinski, M. A., Frey, G., Cui, H., Squire, S., Pagel, M. D., Rodriguez, J. J., Gillies, R. J., & Stopeck, A. T. (2015). Diffusion MRI with SemiAutomated Segmentation Can Serve as a Restricted Predictive Biomarker of the Therapeutic Response of Liver Metastasis. Magnetic Resonance Imaging. doi:10.1016/j.mri.2015.08.006
 Stephen, R. M., Stephen, R. M., Roe, D. J., Roe, D. J., Jha, A. K., Jha, A. K., Trouard, T. P., Trouard, T. P., Galons, J., Galons, J., Kupinski, M. A., Kupinski, M. A., Frey, G., Frey, G., Cui, H., Cui, H., Squire, S., Squire, S., Pagel, M. D., , Pagel, M. D., et al. (2015). Diffusion MRI with SemiAutomaDiffusion MRI with SemiAutomated Segmentation Can Serve as a Restricted Predictive Biomarker of the Therapeutic Response of Liver Metastasis. Magnetic Resonance Imaging. doi:10.1016/j.mri.2015.08.006
 Tseng, H., Fan, J., & Kupinski, M. (2015). SUF20716: CT Protocols Optimization Using Model Observer. Medical physics, 42, 35453545.
 Huang, J., Yuan, Q., Zhang, B., Xu, K., Tankam, P., Clarkson, E., Kupinski, M. A., Hindman, H. B., Aquavella, J. V., Suleski, T. J., & others, . (2014). Measurement of a multilayered tear film phantom using optical coherence tomography and statistical decision theory. Biomedical Optics Express, 5(12), 43744386.
 Myers, K., Bakic, P., Abbey, C., Kupinski, M., & Mertelmeier, T. (2014). TUA17A02: In Memoriam of Ben Galkin: Virtual Tools for Validation of XRay Breast Imaging Systems. Medical Physics, 41(6), 446447.
 Tseng, H., Fan, J., Kupinski, M. A., Sainath, P., & Hsieh, J. (2014). Assessing image quality and dose reduction of a new xray computed tomography iterative reconstruction algorithm using model observers. Medical physics, 41(7), 071910.
 Welge, W. A., DeMarco, A. T., Watson, J. M., Rice, P. S., Barton, J. K., & Kupinski, M. A. (2014). Diagnostic potential of multimodal imaging of ovarian tissue using optical coherence tomography and secondharmonic generation microscopy. Journal of Medical Imaging, 1(2), 025501025501.
 Barrett, H. H., Kupinski, M. A., M\"ueller, S., Halpern, H. J., Morris III, J. C., & Dwyer, R. (2013). Objective assessment of image quality VI: imaging in radiation therapy. Physics in medicine and biology, 58(22), 8197.
 Barrett, H. H., Kupinski, M. A., Müeller, S., Halpern, H. J., Morris, J. C., & Dwyer, R. (2013). Objective assessment of image quality VI: Imaging in radiation therapy. Physics in Medicine and Biology, 58(22), 81978213.More infoAbstract: Earlier work on objective assessment of image quality (OAIQ) focused largely on estimation or classification tasks in which the desired outcome of imaging is accurate diagnosis. This paper develops a general framework for assessing imaging quality on the basis of therapeutic outcomes rather than diagnostic performance. By analogy to receiver operating characteristic (ROC) curves and their variants as used in diagnostic OAIQ, the method proposed here utilizes the therapy operating characteristic or TOC curves, which are plots of the probability of tumor control versus the probability of normaltissue complications as the overall dose level of a radiotherapy treatment is varied. The proposed figure of merit is the area under the TOC curve, denoted AUTOC. This paper reviews an earlier exposition of the theory of TOC and AUTOC, which was specific to the assessment of imagesegmentation algorithms, and extends it to other applications of imaging in externalbeam radiation treatment as well as in treatment with internal radioactive sources. For each application, a methodology for computing the TOC is presented. A key difference between ROC and TOC is that the latter can be defined for a single patient rather than a population of patients. © 2013 Institute of Physics and Engineering in Medicine.
 Dumas, C., Bernstein, A., Espinoza, A., Morgan, D., Lewis, K., Nipper, M., Barrett, H. H., Kupinski, M. A., & Furenlid, L. R. (2013). SmartCAM: An adaptive clinical SPECT camera. Proceedings of SPIE  The International Society for Optical Engineering, 8853.More infoAbstract: An adaptive pinhole aperture that fits a GE MaxiCam SinglePhotonEmission Computed Tomography (SPECT) system has been designed, built, and is undergoing testing. The purpose of an adaptive aperture is to allow the imaging system to make adjustments to the aperture while imaging data are being acquired. Our adaptive pinhole aperture can alter several imaging parameters, including field of view, resolution, sensitivity, and magnification. The dynamic nature of such an aperture allows for imaging of specific regions of interest based on initial measurements of the patient. Ideally, this mode of data collection will improve the understanding of a patient's condition, and will facilitate better diagnosis and treatment. The aperture was constructed using aluminum and a low melting point, highstoppingpower metal alloy called Cerrobend. The aperture utilizes a rotating disk for the selection of a pinhole configuration; as the aluminum disk rotates, different pinholes move into view of the camera face and allow the passage of gamma rays through that particular pinhole. By controlling the angular position of the disk, the optical characteristics of the aperture can be modified, allowing the system to acquire data from controlled regions of interest. First testing was performed with a small radioactive source to prove the functionality of the aperture. © 2013 SPIE.
 Fan, J., Tseng, H., Kupinski, M., Cao, G., Sainath, P., & Hsieh, J. (2013). Study of the radiation dose reduction capability of a CT reconstruction algorithm  LCD performance assessment using mathematical model observers. Proceedings of SPIE  The International Society for Optical Engineering, 8673.More infoAbstract: Radiation dose on patient has become a major concern today for Computed Tomography (CT) imaging in clinical practice. Various hardware and algorithm solutions have been designed to reduce dose. Among them, iterative reconstruction (IR) has been widely expected to be an effective dose reduction approach for CT. However, there is no clear understanding on the exact amount of dose saving an IR approach can offer for various clinical applications. We know that quantitative image quality assessment should be taskbased. This work applied mathematical model observers to study detectability performance of CT scan data reconstructed using an advanced IR approach as well as the conventional filtered backprojection (FBP) approach. The purpose of this work is to establish a practical and robust approach for CT IR detectability image quality evaluation and to assess the dose saving capability of the IR method under study. Low contrast (LC) objects imbedded in head size and body size phantoms were imaged multiple times with different dose levels. Independent signal present and absent pairs were generated for model observer study training and testing. Receiver Operating Characteristic (ROC) curves for location known exact and location ROC (LROC) curves for location unknown as well as their corresponding the area under the curve (AUC) values were calculated. Results showed approximately 3 times dose reduction has been achieved using the IR method under study. © 2013 SPIE.
 Huang, J., Clarkson, E., Kupinski, M., Lee, K., Maki, K. L., Ross, D. S., Aquavella, J. V., & Rolland, J. P. (2013). Maximumlikelihood estimation in Optical Coherence Tomography in the context of the tear film dynamics. Biomedical Optics Express, 4(10), 18061816.More infoPMID: 24156045;PMCID: PMC3799647;Abstract: Understanding tear film dynamics is a prerequisite for advancing the management of Dry Eye Disease (DED). In this paper, we discuss the use of optical coherence tomography (OCT) and statistical decision theory to analyze the tear film dynamics of a digital phantom. We implement a maximumlikelihood (ML) estimator to interpret OCT data based on mathematical models of FourierDomain OCT and the tear film. With the methodology of taskbased assessment, we quantify the tradeoffs among key imaging system parameters. We find, on the assumption that the broadband light source is characterized by circular Gaussian statistics, ML estimates of 40 nm +/ 4 nm for an axial resolution of 1 μm and an integration time of 5 μs. Finally, the estimator is validated with a digital phantom of tear film dynamics, which reveals estimates of nanometer precision. © 2013 Optical Society of America.
 Huang, J., Clarkson, E., Kupinski, M., Lee, K., Maki, K. L., Ross, D. S., Aquavella, J. V., & Rolland, J. P. (2013). Maximumlikelihood estimation in Optical Coherence Tomography in the context of the tear film dynamics. Biomedical optics express, 4(10), 18061816.
 Huang, J., Lee, K., Clarkson, E., Kupinski, M., Maki, K. L., Ross, D. S., Aquavella, J. V., & Rolland, J. P. (2013). Phantom study of tear film dynamics with optical coherence tomography and maximumlikelihood estimation. Optics Letters, 38(10), 17211723.More infoPMID: 23938923;Abstract: In this Letter, we implement a maximumlikelihood estimator to interpret optical coherence tomography (OCT) data for the first time, based on Fourierdomain OCT and a twointerface tear film model. We use the root mean square error as a figure of merit to quantify the system performance of estimating the tear film thickness. With the methodology of taskbased assessment, we study the tradeoff between system imaging speed (temporal resolution of the dynamics) and the precision of the estimation. Finally, the estimator is validated with a digital tearfilm dynamics phantom. © 2013 Optical Society of America.
 Huang, J., Lee, K., Clarkson, E., Kupinski, M., Maki, K. L., Ross, D. S., Aquavella, J. V., & Rolland, J. P. (2013). Phantom study of tear film dynamics with optical coherence tomography and maximumlikelihood estimation. Optics letters, 38(10), 17211723.
 Jha, A. K., Barrett, H. H., Clarkson, E., Caucci, L., & Kupinski, M. A. (2013). Analytic methods for listmode reconstruction. Intl Meet Fully ThreeDim Image Reconstruction Rad Nucl Med, California.
 Jha, A. K., Clarkson, E., & Kupinski, M. A. (2013). An idealobserver framework to investigate signal detectability in diffuse optical imaging. Biomedical Optics Express, 4(10), 21072123.More infoPMID: 24156068;PMCID: PMC3799670;Abstract: With the emergence of diffuse optical tomography (DOT) as a noninvasive imaging modality, there is a requirement to evaluate the performance of the developed DOT systems on clinically relevant tasks. One such important task is the detection of highabsorption signals in the tissue. To investigate signal detectability in DOT systems for system optimization, an appropriate approach is to use the Bayesian ideal observer, but this observer is computationally very intensive. It has been shown that the Fisher information can be used as a surrogate figure of merit (SFoM) that approximates the ideal observer performance. In this paper, we present a theoretical framework to use the Fisher information for investigating signal detectability in DOT systems. The usage of Fisher information requires evaluating the gradient of the photon distribution function with respect to the absorption coefficients. We derive the expressions to compute the gradient of the photon distribution function with respect to the scattering and absorption coefficients. We find that computing these gradients simply requires executing the radiative transport equation with a different source term. We then demonstrate the application of the SFoM to investigate signal detectability in DOT by performing various simulation studies, which help to validate the proposed framework and also present some insights on signal detectability in DOT. © 2013 Optical Society of America.
 Jha, A. K., Clarkson, E., & Kupinski, M. A. (2013). An idealobserver framework to investigate signal detectability in diffuse optical imaging. Biomedical optics express, 4(10), 21072123.
 Jha, A. K., Clarkson, E., Kupinski, M. A., & Barrett, H. H. (2013). Joint reconstruction of activity and attenuation map using LM SPECT emission data. Progress in Biomedical Optics and Imaging  Proceedings of SPIE, 8668.More infoAbstract: Attenuation and scatter correction in single photon emission computed tomography (SPECT) imaging often requires a computed tomography (CT) scan to compute the attenuation map of the patient. This results in increased radiation dose for the patient, and also has other disadvantages such as increased costs and hardware complexity. Attenuation in SPECT is a direct consequence of Compton scattering, and therefore, if the scattered photon data can give information about the attenuation map, then the CT scan may not be required. In this paper, we investigate the possibility of joint reconstruction of the activity and attenuation map using list mode (LM) SPECT emission data, including the scatteredphoton data. We propose a pathbased formalism to process scatteredphoton data. Following this, we derive analytic expressions to compute the CraḿerRao bound (CRB) of the activity and attenuation map estimates, using which, we can explore the fundamental limit of informationretrieval capacity from LM SPECT emission data. We then suggest a maximumlikelihood (ML) scheme that uses the LM emission data to jointly reconstruct the activity and attenuation map. We also propose an expectationmaximization (EM) algorithm to compute the ML solution. © 2013 SPIE.
 Jha, A. K., Dam, H. T., Kupinski, M. A., & Clarkson, E. (2013). Coll. of Opt. Sci., Univ. of Arizona, Tucson, AZ, USA. Nuclear Science, IEEE Transactions on, 60(1), 336351.
 Jha, A. K., Dam, H. T., Kupinski, M. A., & Clarkson, E. (2013). Simulating Silicon Photomultiplier Response to Scintillation Light. IEEE Transactions on Nuclear Science, 60, 336351.
 Jha, A. K., Jha, A. K., Kupinski, M. A., Kupinski, M. A., Rodriguez, J. J., Rodriguez, J. J., Stopeck, A. T., & Stopeck, A. T. (2013). Corrigendum: TaskBased Evaluation of Segmentation Algorithms for DiffusionWeighted MRI without Using a Gold Standard. Physics in Medicine and Biology, 58(1), 183.
 Jha, A. K., Kupinski, M. A., Rodriguez, J. J., Stephen, R. M., & Stopeck, A. T. (2013). Corrigendum: Taskbased evaluation of segmentation algorithms for diffusionweighted MRI without using a gold standard. Physics in Medicine and Biology, 58(1), 183.
 Jha, A. K., T., H., Kupinski, M. A., & Clarkson, E. (2013). Simulating silicon photomultiplier response to scintillation light. IEEE Transactions on Nuclear Science, 60(1), 336351.More infoAbstract: The response of a Silicon Photomultiplier (SiPM) to optical signals is affected by many factors including photondetection efficiency, recovery time, gain, optical crosstalk, afterpulsing, dark count, and detector dead time. Many of these parameters vary with overvoltage and temperature. When used to detect scintillation light, there is a complicated nonlinear relationship between the incident light and the response of the SiPM. In this paper, we propose a combined discretetime discreteevent Monte Carlo (MC) model to simulate SiPM response to scintillation light pulses. Our MC model accounts for all relevant aspects of the SiPM response, some of which were not accounted for in the previous models. We also derive and validate analytic expressions for the singlephotoelectron response of the SiPM and the voltage drop across the quenching resistance in the SiPM microcell. These analytic expressions consider the effect of all the circuit elements in the SiPM and accurately simulate the timevariation in overvoltage across the microcells of the SiPM. Consequently, our MC model is able to incorporate the variation of the different SiPM parameters with varying overvoltage. The MC model is compared with measurements on SiPMbased scintillation detectors and with some cases for which the response is known a priori. The model is also used to study the variation in SiPM behavior with SiPMcircuit parameter variations and to predict the response of a SiPMbased detector to various scintillators. © 19632012 IEEE.
 Kang, D., & Kupinski, M. A. (2013). Figure of merit for taskbased assessment of frequencydomain diffusive imaging. Optics Letters, 38(2), 235237.More infoPMID: 23454973;Abstract: A figure of merit (FOM) for frequencydomain diffusive imaging (FDDI) is theoretically developed adapting the concept of Hotelling observer signaltonoise ratio. Different from conventionally used FOMs for FDDI, the newly developed FOM considers diffused intensities, modulation amplitudes, and phases in combination. The FOM applied to Monte Carlo simulations of signal and backgroundknownexactly problems shows unique characteristics that are in agreement with findings in the literature. We believe that a task based assessment using the FOM improves the characterization of FDDI systems and allows for complete system optimization. © 2013 Optical Society of America.
 Kang, D., & Kupinski, M. A. (2013). Figure of merit for taskbased assessment of frequencydomain diffusive imaging. Optics letters, 38(2), 235237.
 Kupinski, M., Kang, D., & Kupinski, M. A. (2013). Figure of merit for taskbased assessment of frequencydomain diffusive imaging. Optics letters, 38(2).More infoA figure of merit (FOM) for frequencydomain diffusive imaging (FDDI) is theoretically developed adapting the concept of Hotelling observer signaltonoise ratio. Different from conventionally used FOMs for FDDI, the newly developed FOM considers diffused intensities, modulation amplitudes, and phases in combination. The FOM applied to Monte Carlo simulations of signal and backgroundknownexactly problems shows unique characteristics that are in agreement with findings in the literature. We believe that a task based assessment using the FOM improves the characterization of FDDI systems and allows for complete system optimization.
 Lee, C., Kupinski, M. A., & Volokh, L. (2013). Assessment of cardiac singlephoton emission computed tomography performance using a scanning linear observer. Medical Physics, 40(1).More infoPMID: 23298097;PMCID: PMC3581138;Abstract: Purpose: Singlephoton emission computed tomography (SPECT) is widely used to detect myocardial ischemia and myocardial infarction. It is important to assess and compare different SPECT system designs in order to achieve the highest detectability of cardiac defects. Methods: Whitaker 's study ["Estimating random signal parameters from noisy images with nuisance parameters: linear and scanninglinear methods," Opt. Express 16(11), 81508173 (2008)]10.1364/OE.16.008150 on the scanning linear observer (SLO) shows that the SLO can be used to estimate the location and size of signals. One major advantage of the SLO is that it can be used with projection data rather than with reconstruction data. Thus, this observer model assesses the overall hardware performance independent of any reconstruction algorithm. In addition, the computation time of image quality studies is significantly reduced. In this study, three systems based on the design of the GE cadmium zinc telluridebased dedicated cardiac SPECT camera Discovery 530c were assessed. This design, which is officially named the Alcyone Technology: Discovery NM 530c, was commercialized in August, 2009. The three systems, GE27, GE19, and GE13, contain 27, 19, and 13 detectors, respectively. Clinically, a human heart can be virtually segmented into three coronary artery territories: the leftanterior descending artery, leftcircumflex artery, and right coronary artery. One of the most important functions of a cardiac SPECT system is to produce images from which a radiologist can accurately predict in which territory the defect exists [http://www.asnc.org/media/PDFs/PPReporting081511.pdf, Guideline from American Society of Nuclear Cardiology]. A good estimation of the extent of the defect from the projection images is also very helpful for determining the seriousness of the myocardial ischemia. In this study, both the location and extent of defects were estimated by the SLO, and the system performance was assessed by localization receiver operating characteristic (LROC) [P. Khurd and G. Gindi, "Decision strategies maximizing the area under the LROC curve," Proc. SPIE 5749, 150161 (2005)]10.1117/12.595915 or estimation receiver operating characteristic (EROC) [E. Clarkson, "Estimation receiver operating characteristic curve and ideal observers for combined detection/estimation tasks," J. Opt. Soc. Am. A 24, B91B98 (2007)]10.1364/JOSAA.24.000B91 curves. Results: The area under the LROC/EROC curve (AULC/AUEC) and the true positive fraction (TPF) at a specific false positive fraction (FPF) can be treated as the figures of merit. For radii estimation with a 1 mm tolerance, the AUEC values of the GE27, GE19, and GE13 systems are 0.8545, 0.8488, and 0.8329, and the TPF at FPF = 5% are 77.1%, 76.46%, and 73.55%, respectively. The assessment of all three systems revealed that the GE19 system yields estimated information and cardiac defect detectability very close to those of the GE27 system while using eight fewer detectors. Thus, 30% of the expensive detector units can be removed with confidence. Conclusions: As the results show, a combination of the SLO and LROC/EROC curves can determine the configuration that yields the most relevant estimation/detection information. Thus, this is a useful method for assessing cardiac SPECT systems. © 2013 American Association of Physicists in Medicine.
 Lee, C., Kupinski, M. A., & Volokh, L. (2013). Assessment of cardiac singlephoton emission computed tomography performance using a scanning linear observer. Medical physics, 40(1), 011906.
 Tseng, H., Fan, J., Kupinski, M., Sainath, P., & Hsieh, J. (2013). TUC10305: Image Quality and Dose Reduction Evaluation of a New CT Iterative Reconstruction Algorithm Using Model Observers. Medical Physics, 40(6), 437437.
 Huang, J., Lee, K., Clarkson, E., Kupinski, M., & Rolland, J. P. (2012). Quantitative measurement of tear film dynamics with optical coherence tomography and statistical decision theory. Journal of Vision, 12(14), 3939.
 Huang, J., Lee, K., Clarkson, E., Kupinski, M., & Rolland, J. P. (2012). Taskbased assessment and optimization of spectral domain optical coherence tomography for tear film imaging. Frontiers in Optics, FIO 2012.More infoAbstract: Using taskbased assessment method, we adopt detectability as a performance metric to evaluate and optimize spectral domain optical coherence tomography (SDOCT) for tear film imaging. © OSA 2012.
 Jha, A. K., Kupinski, M. A., & T., H. (2012). Monte Carlo simulation of silicon photomultiplier output in response to scintillation induced light. IEEE Nuclear Science Symposium Conference Record, 16931696.More infoAbstract: The response of a Silicon Photomultiplier (SiPM) to optical signals is affected by many factors including optical cross talk, afterpulsing, dark current, detector dead time, recovery time and gain. Many of these parameters vary with overvoltage. When used to detect scintillation light, it is difficult to relate the response of the SiPM with the incident light and the relationship can be highly nonlinear. In this paper, we propose a Monte Carlo (MC) model for simulating the response of the SiPM to scintillation induced light pulses, which can be used to relate the optical signal with the SiPM response. Developing further on the previous works in this field, the model simulates the various aspects of SiPM response, including photon detection efficiency, recovery time, gain variation and dead time while accounting for the temporal and statistical distribution of the incident light, optical crosstalk, afterpulsing and dark current. It also considers the variation of the different SiPM parameters with varying overvoltage. We have also derived analytic expressions for the single photon response and the voltage drop across the quenching resistance, that help in accurate simulation of the SiPM response. The model compares well with the measurements on a SiPM based scintillation detector. It is also in agreement with the expected mathematical response when the input is an instantaneous light pulse. © 2011 IEEE.
 Jha, A. K., Kupinski, M. A., Barrett, H. H., Clarkson, E., & Hartman, J. H. (2012). Threedimensional Neumannseries approach to model light transport in nonuniform media. JOSA A, 29(9), 18851899.
 Jha, A. K., Kupinski, M. A., Masumura, T., Clarkson, E., Maslov, A. V., & Barrett, H. H. (2012). Simulating photontransport in uniform media using the radiative transport equation: A study using the Neumannseries approach. Journal of the Optical Society of America A: Optics and Image Science, and Vision, 29(8), 17411757.More infoPMID: 23201893;PMCID: PMC3985394;Abstract: We present the implementation, validation, and performance of a Neumannseries approach for simulating light propagation at optical wavelengths in uniform media using the radiative transport equation (RTE). The RTE is solved for an anisotropicscattering medium in a spherical harmonic basis for a diffuseopticalimaging setup. The main objectives of this paper are threefold: to present the theory behind the Neumannseries form for the RTE, to design and develop the mathematical methods and the software to implement the Neumann series for a diffuseopticalimaging setup, and, finally, to perform an exhaustive study of the accuracy, practical limitations, and computational efficiency of the Neumannseries method. Through our results, we demonstrate that the Neumannseries approach can be used to model light propagation in uniform media with small geometries at optical wavelengths. © 2012 Optical Society of America.
 Jha, A. K., Kupinski, M. A., Masumura, T., Clarkson, E., Maslov, A. V., & Barrett, H. H. (2012). Simulating photontransport in uniform media using the radiative transport equation: a study using the Neumannseries approach. JOSA A, 29(8), 17411757.
 Jha, A. K., Kupinski, M. A., Rodr\'\iguez, J. J., Stephen, R. M., & Stopeck, A. T. (2012). Taskbased evaluation of segmentation algorithms for diffusionweighted MRI without using a gold standard. Physics in medicine and biology, 57(13), 4425.
 Jha, A. K., Kupinski, M. A., Rodriguez, J. J., Stephen, R. M., & Stopeck, A. T. (2012). TaskBased Evaluation of Segmentation Algorithms for DiffusionWeighted MRI without Using a Gold Standard. Physics in Medicine and Biology, 57(13), 44254446.
 Kang, D., & Kupinski, M. A. (2012). Effect of noise on modulation amplitude and phase in frequencydomain diffusive imaging. Journal of Biomedical Optics, 17(1).More infoPMID: 22352660;Abstract: We theoretically investigate the effect of noise on frequencydomain heterodyne and/or homodyne measurements of intensitymodulated beams propagating through diffusive media, such as a photon density wave. We assumed that the attenuated amplitude and delayed phase are estimated by taking the Fourier transform of the noisy, modulated output data. We show that the estimated amplitude and phase are biased when the number of output photons is small. We also show that the use of image intensifiers for photon amplification in heterodyne or homodyne measurements increases the amount of biases. Especially, it turns out that the biased estimation is independent of ACdependent noise in sinusoidal heterodyne or homodyne outputs. Finally, the developed theory indicates that the previously known variance model of modulation amplitude and phase is not valid in low light situations. MonteCarlo simulations with varied numbers of input photons verify our theoretical trends of the bias. © 2012 Society of PhotoOptical Instrumentation Engineers (SPIE).
 Kang, D., & Kupinski, M. A. (2012). Effect of noise on modulation amplitude and phase in frequencydomain diffusive imaging. Journal of biomedical optics, 17(1), 016010101601010.
 Kang, D., & Kupinski, M. A. (2012). Noise characteristics of heterodyne/homodyne frequencydomain measurements. Journal of Biomedical Optics, 17(1).More infoPMID: 22352646;PMCID: PMC3603149;Abstract: We theoretically develop and experimentally validate the noise characteristics of heterodyne and/or homodyne measurements that are widely used in frequencydomain diffusive imaging. The mean and covariance of the modulated heterodyne output are derived by adapting the random amplification of a temporal point process. A multinomial selection rule is applied to the result of the temporal noise analysis to additionally model the spatial distribution of intensified photons measured by a chargecoupled device (CCD), which shows that the photon detection efficiency of CCD pixels plays an important role in the noise property of detected photons. The approach of using a multinomial probability law is validated from experimental results. Also, experimentally measured characteristics of means and variances of homodyne outputs are in agreement with the developed theory. The developed noise model can be applied to all photon amplification processes. © 2012 Society of PhotoOptical Instrumentation Engineers (SPIE).
 Kang, D., & Kupinski, M. A. (2012). Noise characteristics of heterodyne/homodyne frequencydomain measurements. Journal of biomedical optics, 17(1), 015002101500211.
 Kupinski, M., Kang, D., & Kupinski, M. A. (2012). Effect of noise on modulation amplitude and phase in frequencydomain diffusive imaging. Journal of biomedical optics, 17(1).More infoWe theoretically investigate the effect of noise on frequencydomain heterodyne and/or homodyne measurements of intensitymodulated beams propagating through diffusive media, such as a photon density wave. We assumed that the attenuated amplitude and delayed phase are estimated by taking the Fourier transform of the noisy, modulated output data. We show that the estimated amplitude and phase are biased when the number of output photons is small. We also show that the use of image intensifiers for photon amplification in heterodyne or homodyne measurements increases the amount of biases. Especially, it turns out that the biased estimation is independent of ACdependent noise in sinusoidal heterodyne or homodyne outputs. Finally, the developed theory indicates that the previously known variance model of modulation amplitude and phase is not valid in low light situations. MonteCarlo simulations with varied numbers of input photons verify our theoretical trends of the bias.
 Kupinski, M., Kang, D., & Kupinski, M. A. (2012). Noise characteristics of heterodyne/homodyne frequencydomain measurements. Journal of biomedical optics, 17(1).More infoWe theoretically develop and experimentally validate the noise characteristics of heterodyne and/or homodyne measurements that are widely used in frequencydomain diffusive imaging. The mean and covariance of the modulated heterodyne output are derived by adapting the random amplification of a temporal point process. A multinomial selection rule is applied to the result of the temporal noise analysis to additionally model the spatial distribution of intensified photons measured by a chargecoupled device (CCD), which shows that the photon detection efficiency of CCD pixels plays an important role in the noise property of detected photons. The approach of using a multinomial probability law is validated from experimental results. Also, experimentally measured characteristics of means and variances of homodyne outputs are in agreement with the developed theory. The developed noise model can be applied to all photon amplification processes.
 Clarkson, E., Palit, R., & Kupinski, M. A. (2011). SVD for imaging systems with discrete rotational symmetry. Optics InfoBase Conference Papers.More infoAbstract: In the presence of discrete rotational symmetry for a tomographic imaging system we show that the dimension of the SVD computation can be reduced by a factor equal to the number of collection angles. © 2011 OSA.
 Kang, D., & Kupinski, M. A. (2011). Signal detectability in diffusive media using phased arrays in conjunction with detector arrays. Optics Express, 19(13), 1226112274.More infoPMID: 21716463;Abstract: We investigate Hotelling observer performance (i.e., signal detectability) of a phased array system for tasks of detecting small inhomogeneities and distinguishing adjacent abnormalities in uniform diffusive media. Unlike conventional phased array systems where a single detector is located on the interface between two sources, we consider a detector array, such as a CCD, on a phantom exit surface for calculating the Hotelling observer detectability. The signal detectability for adjacent small abnormalities (2mm displacement) for the CCDbased phased array is related to the resolution of reconstructed images. Simulations show that acquiring highdimensional data from a detector array in a phased array system dramatically improves the detectability for both tasks when compared to conventional single detector measurements, especially at low modulation frequencies. It is also observed in all studied cases that there exists the modulation frequency optimizing CCDbased phased array systems, where detectability for both tasks is consistently high. These results imply that the CCDbased phased array has the potential to achieve high resolution and signal detectability in tomographic diffusive imaging while operating at a very low modulation frequency. The effect of other configuration parameters, such as a detector pixel size, on the observer performance is also discussed. © 2011 Optical Society of America.
 Kang, D., & Kupinski, M. A. (2011). Signal detectability in diffusive media using phased arrays in conjunction with detector arrays. Optics express, 19(13), 1226112274.
 Kupinski, M., Kang, D., & Kupinski, M. A. (2011). Signal detectability in diffusive media using phased arrays in conjunction with detector arrays. Optics express, 19(13).More infoWe investigate Hotelling observer performance (i.e., signal detectability) of a phased array system for tasks of detecting small inhomogeneities and distinguishing adjacent abnormalities in uniform diffusive media. Unlike conventional phased array systems where a single detector is located on the interface between two sources, we consider a detector array, such as a CCD, on a phantom exit surface for calculating the Hotelling observer detectability. The signal detectability for adjacent small abnormalities (2 mm displacement) for the CCDbased phased array is related to the resolution of reconstructed images. Simulations show that acquiring highdimensional data from a detector array in a phased array system dramatically improves the detectability for both tasks when compared to conventional single detector measurements, especially at low modulation frequencies. It is also observed in all studied cases that there exists the modulation frequency optimizing CCDbased phased array systems, where detectability for both tasks is consistently high. These results imply that the CCDbased phased array has the potential to achieve high resolution and signal detectability in tomographic diffusive imaging while operating at a very low modulation frequency. The effect of other configuration parameters, such as a detector pixel size, on the observer performance is also discussed.
 Barrett, H. H., Wilson, D. W., Kupinski, M. A., Aguwa, K., Ewell, L., Hunter, R., & Müller, S. (2010). Therapy operating characteristic (TOC) curves and their application to the evaluation of segmentation algorithms. Progress in Biomedical Optics and Imaging  Proceedings of SPIE, 7627.More infoAbstract: This paper presents a general framework for assessing imaging systems and imageanalysis methods on the basis of therapeutic rather than diagnostic efficacy. By analogy to receiver operating characteristic (ROC) curves, it introduces the Therapy Operating Characteristic or TOC curve, which is a plot of the probability of tumor control vs. the probability of normaltissue complications as the overall level of a radiotherapy treatment beam is varied. The proposed figure of merit is the area under the TOC, denoted AUTOC. If the treatment planning algorithm is held constant, AUTOC is a metric for the imaging and imageanalysis components, and in particular for segmentation algorithms that are used to delineate tumors and normal tissues. On the other hand, for a given set of segmented images, AUTOC can also be used as a metric for the treatment plan itself. A general mathematical theory of TOC and AUTOC is presented and then specialized to segmentation problems. Practical approaches to implementation of the theory in both simulation and clinical studies are presented. The method is illustrated with a a brief study of segmentation methods for prostate cancer. © 2010 Copyright SPIE  The International Society for Optical Engineering.
 Clarkson, E., Palit, R., & Kupinski, M. A. (2010). SVD for imaging systems with discrete rotational symmetry. Optics Express, 18(24), 2530625320.More infoPMID: 21164879;PMCID: PMC3027225;Abstract: The singular value decomposition (SVD) of an imaging system is a computationally intensive calculation for tomographic imaging systems due to the large dimensionality of the system matrix. The computation often involves memory and storage requirements beyond those available to most end users. We have developed a method that reduces the dimension of the SVD problem towards the goal of making the calculation tractable for a standard desktop computer. In the presence of discrete rotational symmetry we show that the dimension of the SVD computation can be reduced by a factor equal to the number of collection angles for the tomographic system. In this paper we present the mathematical theory for our method, validate that our method produces the same results as standard SVD analysis, and finally apply our technique to the sensitivity matrix for a clinical CT system. The ability to compute the full singular value spectra and singular vectors could augment future work in system characterization, imagequality assessment and reconstruction techniques for tomographic imaging systems. © 2010 Optical Society of America.
 Clarkson, E., Palit, R., & Kupinski, M. A. (2010). SVD for imaging systems with discrete rotational symmetry. Optics express, 18(24), 2530625320.
 Hesterman, J. Y., Caucci, L., Kupinski, M. A., Barrett, H. H., & Furenlid, L. R. (2010). Maximumlikelihood estimation with a contractinggrid search algorithm. IEEE Transactions on Nuclear Science, 57(3 PART 1), 10771084.More infoAbstract: A fast search algorithm capable of operating in multidimensional spaces is introduced. As a sample application, we demonstrate its utility in the 2D and 3D maximumlikelihood positionestimation problem that arises in the processing of PMT signals to derive interaction locations in compact gamma cameras. We demonstrate that the algorithm can be parallelized in pipelines, and thereby efficiently implemented in specialized hardware, such as fieldprogrammable gate arrays (FPGAs). A 2D implementation of the algorithm is achieved in Cell/BE processors, resulting in processing speeds above one million events per second, which is a 20 × increase in speed over a conventional desktop machine. Graphics processing units (GPUs) are used for a 3D application of the algorithm, resulting in processing speeds of nearly 250,000 events per second which is a 250 × increase in speed over a conventional desktop machine. These implementations indicate the viability of the algorithm for use in realtime imaging applications. © 2010 IEEE.
 Hesterman, J. Y., Caucci, L., Kupinski, M. A., Barrett, H. H., & Furenlid, L. R. (2010). Maximumlikelihood estimation with a contractinggrid search algorithm. Nuclear Science, IEEE Transactions on, 57(3), 10771084.
 Jha, A. K., Kupinski, M. A., Rodríguez, J. J., Stephen, R. M., & Stopeck, A. T. (2010). ADC estimation in multiscan DWMRI. Optics InfoBase Conference Papers.More infoAbstract: A maximumlikelihoodbased scheme for estimating the Apparent Diffusion Coefficient (ADC) value in diffusionweighted MRI is presented, using which data from multiple scans acquired at the same diffusiongradient value can be used for accurate ADC computation. © 2010 Optical Society of America.
 Kupinski, M., Clarkson, E., Palit, R., & Kupinski, M. A. (2010). SVD for imaging systems with discrete rotational symmetry. Optics express, 18(24).More infoThe singular value decomposition (SVD) of an imaging system is a computationally intensive calculation for tomographic imaging systems due to the large dimensionality of the system matrix. The computation often involves memory and storage requirements beyond those available to most end users. We have developed a method that reduces the dimension of the SVD problem towards the goal of making the calculation tractable for a standard desktop computer. In the presence of discrete rotational symmetry we show that the dimension of the SVD computation can be reduced by a factor equal to the number of collection angles for the tomographic system. In this paper we present the mathematical theory for our method, validate that our method produces the same results as standard SVD analysis, and finally apply our technique to the sensitivity matrix for a clinical CT system. The ability to compute the full singular value spectra and singular vectors will augment future work in system characterization, imagequality assessment and reconstruction techniques for tomographic imaging systems.
 Clarkson, E., & Kupinski, M. A. (2009). Global compartmental pharmacokinetic models for spatiotemporal SPECT and PET imaging. SIAM journal on imaging sciences, 2(1), 203225.
 Kupinski, M., Clarkson, E., & Kupinski, M. A. (2009). Global Compartmental Pharmacokinetic Models for Spatiotemporal SPECT and PET Imaging. SIAM journal on imaging sciences, 2(1).More infoA new mathematical framework is introduced for combining the linear compartmental models used in pharmacokinetics with the spatiotemporal distributions of activity that are measured in single photon emission computed tomography (SPECT) and PET imaging. This approach is global in the sense that the compartmental differential equations involve only the overall spatially integrated activity in each compartment. The kinetics for the local compartmental activities are not specified by the model and would be determined from data. It is shown that an increase in information about the spatial distribution of the local compartmental activities leads to an increase in the number of identifiable quantities associated with the compartmental matrix. These identifiable quantities, which are important kinetic parameters in applications, are determined by computing the invariants of a symmetry group. This group generates the space of compartmental matrices that are compatible with a given activity distribution, input function, and set of support constraints. An example is provided where all of the compartmental spatial supports have been separated, except that of the vascular compartment. The question of estimating the identifiable parameters from SPECT and PET data is also discussed.
 Palit, R., Kupinski, M. A., Barrett, H. H., Clarkson, E. W., Aarsvold, J. N., Volokh, L., & Grobshtein, Y. (2009). Singular value decomposition of pinhole SPECT systems. Progress in Biomedical Optics and Imaging  Proceedings of SPIE, 7263.More infoAbstract: A single photon emission computed tomography (SPECT) imaging system can be modeled by a linear operator H that maps from object space to detector pixels in image space. The singular vectors and singularvalue spectra of H provide useful tools for assessing system performance. The number of voxels used to discretize object space and the number of collection angles and pixels used to measure image space make the matrix dimensions H large. As a result, H must be stored sparsely which renders several conventional singular value decomposition (SVD) methods impractical. We used an iterative power methods SVD algorithm (Lanczos) designed to operate on very large sparsely stored matrices to calculate the singular vectors and singularvalue spectra for two small animal pinhole SPECT imaging systems: FastSPECT II and M3R. The FastSPECT II system consisted of two rings of eight scintillation cameras each. The resulting dimensions of H were 68921 voxels by 97344 detector pixels. The M3R system is a four camera system that was reconfigured to measure image space using a single scintillation camera. The resulting dimensions of H were 50864 voxels by 6241 detector pixels. In this paper we present results of the SVD of each system and discuss calculation of the measurement and null space for each system.
 Barrett, H. H., Furenlid, L. R., Freed, M., Hesterman, J. Y., Kupinski, M. A., Clarkson, E., & Whitaker, M. K. (2008). Adaptive SPECT. IEEE Transactions on Medical Imaging, 27(6), 775788.More infoPMID: 18541485;PMCID: PMC2575754;Abstract: Adaptive imaging systems alter their dataacquisition configuration or protocol in response to the image information received. An adaptive pinhole singlephoton emission computed tomography (SPECT) system might acquire an initial scout image to obtain preliminary information about the radiotracer distribution and then adjust the configuration or sizes of the pinholes, the magnifications, or the projection angles in order to improve performance. This paper briefly describes two smallanimal SPECT systems that allow this flexibility and then presents a framework for evaluating adaptive systems in general, and adaptive SPECT systems in particular. The evaluation is in terms of the performance of linear observers on detection or estimation tasks. Expressions are derived for the ideal linear (Hotelling) observer and the ideal linear (Wiener) estimator with adaptive imaging. Detailed expressions for the performance figures of merit are given, and possible adaptation rules are discussed. © 2006 IEEE.
 Barrett, H. H., Furenlid, L. R., Freed, M., Hesterman, J. Y., Kupinski, M. A., Clarkson, E., & Whitaker, M. K. (2008). Adaptive SPECT. Medical Imaging, IEEE Transactions on, 27(6), 775788.
 Caucci, L., Kupinski, M. A., Freed, M., Furenlid, L. R., Wilson, D. W., & Barrett, H. H. (2008). Adaptive SPECT for tumor necrosis detection. IEEE Nuclear Science Symposium Conference Record, 55485551.More infoAbstract: In this paper, we consider a prototype of an adaptive SPECT system, and we use simulation to objectively assess the system's performance with respect to a conventional, nonadaptive SPECT system. Objective performance assessment is investigated for a clinically relevant task: the detection of tumor necrosis at a known location and in a random lumpy background. The iterative maximumlikelihood expectationmaximization (MLEM) algorithm is used to perform image reconstruction. We carried out human observer studies on the reconstructed images and compared the probability of correct detection when the data are generated with the adaptive system as opposed to the nonadaptive system. Task performance is also assessed by using a channelized Hotelling observer, and the area under the receiver operating characteristic curve is the figure of merit for the detection task. Our results show a large performance improvement of adaptive systems versus nonadaptive systems and motivate further research in adaptive medical imaging. © 2008 IEEE.
 Clarkson, E., Kupinski, M. A., Barrett, H. H., & Furenlid, L. (2008). A taskbased approach to adaptive and multimodality imaging. Proceedings of the IEEE, 96(3), 500511.
 Clarkson, E., Kupinski, M. A., Barrett, H. H., & Furenlid, L. (2008). A taskbased approach to adaptive and multimodality imaging. Proceedings of the IEEE, 96(3), 500511.More infoAbstract: Multimodality imaging is becoming increasingly important in medical imaging. Since the motivation for combining multiple imaging modalities is generally to improve diagnostic or prognostic accuracy, the benefits of multimodality imaging cannot be assessed through the display of example images. Instead, we must use objective, taskbased measures of image quality to draw valid conclusions about system performance. In this paper, we will present a general framework for utilizing objective, taskbased measures of image quality in assessing multimodality and adaptive imaging systems. We introduce a classification scheme for multimodality and adaptive imaging systems and provide a mathematical description of the imaging chain along with block diagrams to provide a visual illustration. We show that the taskbased methodology developed for evaluating singlemodality imaging can be applied, with minor modifications, to multimodality and adaptive imaging. We discuss strategies for practical implementing of taskbased methods to assess and optimize multimodality imaging systems. © 2006 IEEE.
 Freed, M., Kupinski, M. A., Furenlid, L. R., Wilson, D. W., & Barrett, H. H. (2008). A prototype instrument for single pinhole small animal adaptive SPECT imaging. Medical Physics, 35(5), 19121925.More infoPMID: 18561667;PMCID: PMC2575412;Abstract: The authors have designed and constructed a smallanimal adaptive SPECT imaging system as a prototype for quantifying the potential benefit of adaptive SPECT imaging over the traditional fixed geometry approach. The optical design of the system is based on filling the detector with the region of interest for each viewing angle, maximizing the sensitivity, and optimizing the resolution in the projection images. Additional feedback rules for determining the optimal geometry of the system can be easily added to the existing control software. Preliminary data have been taken of a phantom with a small, hot, offset lesion in a flat background in both adaptive and fixed geometry modes. Comparison of the predicted system behavior with the actual system behavior is presented, along with recommendations for system improvements. © 2008 American Association of Physicists in Medicine.
 Freed, M., Kupinski, M. A., Furenlid, L. R., Wilson, D. W., & Barrett, H. H. (2008). A prototype instrument for single pinhole small animal adaptive SPECT imaging. Medical physics, 35(5), 19121925.
 Furenlid, L. R., Moore, J. W., Freed, M., Kupinski, M. A., Clarkson, E., Liu, Z., Wilson, D. W., Woolfenden, J. M., & Barrett, H. H. (2008). Adaptive smallanimal SPECT/CT. 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI, 14071410.More infoAbstract: We are exploring the concept of adaptive multimodality imaging, a form of nonlinear optimization where the imaging configuration is automatically adjusted in response to the object. Preliminary studies suggest that substantial improvement in objective, taskbased measures of image quality can result. We describe here our work to add motorized adjustment capabilities and a matching CT to our existing FastSPECT II system to form an adaptive smallanimal SPECT/CT. ©2008 IEEE.
 Breme, A., Kupinski, M., Clarkson, E., & Barrett, H. (2007). Adaptive Hotelling discriminant functions [651516]. PROCEEDINGSSPIE THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING, 6515, 65150T.
 Brème, A., Kupinski, M. A., Clarkson, E., & Barrett, H. H. (2007). Adaptive hotelling discriminant functions. Progress in Biomedical Optics and Imaging  Proceedings of SPIE, 6515.More infoAbstract: Any observer performing a detection task on an image produces a single number that represents the observer's confidence that a signal (e.g., a tumor) is present. A linear observer produces this test statistic using a linear template or a linear discriminant. The optimal linear discriminant is wellknown to be the Hotelling observer and uses both first and secondorder statistics of the image data. There are many situations where it is advantageous to consider discriminant functions that adapt themselves to some characteristics of the data. In these situations, the linear template is itself a function of the data and, thus, the observer is nonlinear. In this paper, we present an example adaptive Hotelling discriminant and compare the performance of this observer to that of the Hotelling observer and the Bayesian ideal observer. The task is to detect a signal that is imbedded in one of a finite number of possible random backgrounds. Each random background is Gaussian but has different covariance properties. The observer uses the image data to determine which background type is present and then uses the template appropriate for that background. We show that the performance of this particular observer falls between that of Hotelling and ideal observers.
 Freed, M., Kupinski, M. A., Furenlid, L. R., & Barrett, H. H. (2007). A prototype instrument for adaptive SPECT imaging. Progress in Biomedical Optics and Imaging  Proceedings of SPIE, 6510(PART 1).More infoAbstract: We have designed and constructed a smallanimal adaptive SPECT imaging system as a prototype for quantifying the potential benefit of adaptive SPECT imaging over the traditional fixed geometry approach. The optical design of the system is based on filling the detector with the object for each viewing angle, maximizing the sensitivity, and optimizing the resolution in the projection images. Additional feedback rules for determining the optimal geometry of the system can be easily added to the existing control software. Preliminary data have been taken of a phantom with a small, hot, offset lesion in a flat background in both adaptive and fixed geometry modes. Comparison of the predicted system behavior with the actual system behavior is presented along with recommendations for system improvements.
 Hagen, N., Kupinski, M., & Dereniak, E. L. (2007). Gaussian profile estimation in one dimension. Applied optics, 46(22), 53745383.
 Hesterman, J. Y., Kupinski, M. A., Clarkson, E., & Barrett, H. H. (2007). Hardware assessment using the multimodule, multiresolution system (M ^{3} R): A signaldetection study. Medical Physics, 34(7), 30343044.More infoPMID: 17822011;PMCID: PMC2471875;Abstract: The multimodule, multiresolution system (M3 R) is used for hardware assessment in objective, taskbased signal detection studies in projection data. A phantom capable of generating multiple realizations of a random textured background is introduced. Measured backgrounds from this phantom are used along with simulated lumpy and uniform backgrounds to investigate signaltonoise ratio as a function of exposure time. Results are shown to agree with theoretical predictions, exhibiting a powerlaw like dependence previously seen for studies performed either in simulation or without an imaging system, and help validate the use of simulated lumpy backgrounds in observer studies. A second study looks at signaldetection performance, measured by AUC (area under the receiver operating characteristic curve), in lumpy backgrounds for 20 M 3 R aperture combinations as a function of lump size and signal size. Observer performance reveals an improvement in AUC for certain ranges of signal and lump combinations through the use of multiplexed, multiplepinhole apertures, indicating a need for taskspecific aperture optimization. The channelized Hotelling observer is used with LaguerreGauss channels for both observer studies. Methods for selection of number of channels and channel width are discussed. © 2007 American Association of Physicists in Medicine.
 Hesterman, J. Y., Kupinski, M. A., Clarkson, E., & Barrett, H. H. (2007). Hardware assessment using the multimodule, multiresolution system (M3R): A signaldetection study. Medical physics, 34(7), 30343044.
 Hesterman, J. Y., Kupinski, M. A., Clarkson, E., & Whitaker, M. K. (2007). Adaptive SPECT.
 Hesterman, J. Y., Kupinski, M. A., Clarkson, E., Wilson, D. W., & Barrett, H. H. (2007). Evaluation of hardware in a smallanimal SPECT system using reconstructed images. Progress in Biomedical Optics and Imaging  Proceedings of SPIE, 6515.More infoAbstract: Evaluation of imaging hardware represents a vital component of system design. In smallanimal SPECT imaging, this evaluation has become increasingly difficult with the emergence of multipinhole apertures and adaptive, or patientspecific, imaging. This paper will describe two methods for hardware evaluation using reconstructed images. The first method is a rapid technique incorporating a systemspecific nonlinear, threedimensional point response. This point response is easily computed and offers qualitative insight into an aperture's resolution and artifact characteristics. The second method is an objective assessment of signal detection in lumpy backgrounds using the channelized Hotelling observer (CHO) with 3D LaguerreGauss and differenceofGaussian channels to calculate area under the receiveroperating characteristic curve (AUC). Previous work presented at this meeting described a unique, smallanimal SPECT system (M 3R) capable of operating under a myriad of hardware configurations and ideally suited for image quality studies. Measured system matrices were collected for several hardware configurations of M 3R. The data used to implement these two methods was then generated by taking simulated objects through the measured system matrices. The results of these two methods comprise a combination of qualitative and quantitative analysis that is wellsuited for hardware assessment.
 Hesterman, J. Y., Kupinski, M. A., Furenlid, L. R., Wilson, D. W., & Barrett, H. H. (2007). The multimodule, multiresolution system (M3 R): A novel smallanimal SPECT system. Medical Physics, 34(3), 987993.More infoPMID: 17441245;PMCID: PMC2517228;Abstract: We have designed and built an inexpensive, highresolution, tomographic imaging system, dubbed the multimodule, multiresolution system, or M3 R. Slots machined into the system shielding allow for the interchange of pinhole plates, enabling the system to operate over a wide range of magnifications and with virtually any desired pinhole configuration. The flexibility of the system allows system optimization for specific imaging tasks and also allows for modifications necessary due to improved detectors, electronics, and knowledge of system construction (e.g., system sensitivity optimization). We provide an overview of M3 R, focusing primarily on system design and construction, aperture construction, and calibration methods. Reconstruction algorithms will be described and reconstructed images presented. © American Association of Physicists in Medicine.
 Hesterman, J. Y., Kupinski, M. A., Furenlid, L. R., Wilson, D. W., & Barrett, H. H. (2007). The multimodule, multiresolution system (M3R): a novel smallanimal SPECT system. Medical physics, 34(3), 987993.
 Hesterman, J., Kupinski, M., Clarkson, E., Wilson, D., & Barrett, H. (2007). Evaluation of hardware in a smallanimal SPECT system using reconstructed images [651536]. PROCEEDINGSSPIE THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING, 6515, 65151G.
 Kupinski, M. A., Clarkson, E., & Hesterman, J. Y. (2007). Bias in hotelling observer performance computed from finite data. Progress in Biomedical Optics and Imaging  Proceedings of SPIE, 6515.More infoAbstract: An observer performing a detection task analyzes an image and produces a single number, a test statistic, for that image. This test statistic represents the observers "confidence" that a signal (e.g., a tumor) is present. The linear observer that maximizes the teststatistic SNR is known as the Hotelling observer. Generally, computation of the Hotelling SNR, or Hotelling trace, requires the inverse of a large covariance matrix. Recent developments have resulted in methods for the estimation and inversion of these large covariance matrices with relatively small numbers of images. The estimation and inversion of these matrices is made possible by a covariancematrix decomposition that splits the full covariance matrix into an average detectornoise component and a backgroundvariability component. Because the average detectornoise component is often diagonal and/or easily estimated, a fullrank, invertible covariance matrix can be produced with few images. We have studied the bias of estimates of the Hotelling trace using this decomposition for highdetectornoise and lowdetectornoise situations. In extremely lownoise situations, this covariance decomposition may result in a significant bias. We will present a theoretical evaluation of the Hotellingtrace bias, as well as extensive simulation studies.
 Kupinski, M. A., Watson, A. B., Siewerdsen, J. H., Myers, K. J., & Eckstein, M. (2007). Image Quality. JOSA A, 24(12), IQ1IQ1.
 Kupinski, M. A., Watson, A. B., Siewerdsen, J. H., Myers, K. J., & Eckstein, M. (2007). Image quality: Introduction. Journal of the Optical Society of America A: Optics and Image Science, and Vision, 24(12).
 Park, S., Barrett, H. H., Clarkson, E., Kupinski, M. A., & Myers, K. J. (2007). Channelizedideal observer using LaguerreGauss channels in detection tasks involving nonGaussian distributed lumpy backgrounds and a Gaussian signal. JOSA A, 24(12), B136B150.
 Park, S., Barrett, H. H., Clarkson, E., Kupinski, M. A., & Myers, K. J. (2007). Channelizedideal observer using LaguerreGauss channels in detection tasks involving nonGaussian distributed lumpy backgrounds and a Gaussian signal. Journal of the Optical Society of America A: Optics and Image Science, and Vision, 24(12), B136B150.More infoPMID: 18059906;PMCID: PMC2655642;Abstract: We investigate a channelizedideal observer (CIO) with LaguerreGauss (LG) channels to approximate idealobserver performance in detection tasks involving nonGaussian distributed lumpy backgrounds and a Gaussian signal. A Markovchain Monte Carlo approach is employed to determine the performance of both the ideal observer and the CIO using a large number of LG channels. Our results indicate that the CIO with LG channels can approximate idealobserver performance within error bars, depending on the imaging system, object, and channel parameters. The CIO also outperforms a channelizedHotelling observer using the same channels. In addition, an alternative approach for estimating the CIO is investigated. This approach makes use of the characteristic functions of channelized data and employs an approximation method to the area under the receiver operating characteristic curve. The alternative approach provides good estimates of the performance of the CIO with five LG channels. However, for large channel cases, more efficient computational methods need to be developed for the CIO to become useful in practice. © 2007 Optical Society of America.
 Clarkson, E., Kupinski, M. A., & Barrett, H. H. (2006). A Probabilistic Model for the MRMC Method, Part 1: Theoretical Development. Academic Radiology, 13(11), 14101421.More infoPMID: 17070460;PMCID: PMC2844793;Abstract: Rationale and Objectives: Current approaches to receiver operating characteristic (ROC) analysis use the MRMC (multiplereader, multiplecase) paradigm in which several readers read each case and their ratings (or scores) are used to construct an estimate of the area under the ROC curve or some other ROCrelated parameter. Standard practice is to decompose the parameter of interest according to a linear model into terms that depend in various ways on the readers, cases, and modalities. Though the methodologic aspects of MRMC analysis have been studied in detail, the literature on the probabilistic basis of the individual terms is sparse. In particular, few articles state what probability law applies to each term and what underlying assumptions are needed for the assumed independence. When probability distributions are specified for these terms, these distributions are assumed to be Gaussians. Materials and Methods: This article approaches the MRMC problem from a mechanistic perspective. For a single modality, three sources of randomness are included: the images, the reader skill, and the reader uncertainty. The probability law on the reader scores is written in terms of three nested conditional probabilities, and random variables associated with this probability are referred to as triply stochastic. Results: In this article, we present the probabilistic MRMC model and apply this model to the Wilcoxon statistic. The result is a seventerm expansion for the variance of the figure of merit. Conclusion: We relate the terms in this expansion to those in the standard, linear MRMC model. Finally, we use the probabilistic model to derive constraints on the coefficients in the seventerm expansion. © 2006 AUR.
 Clarkson, E., Kupinski, M. A., & Barrett, H. H. (2006). A probabilistic model for the MRMC method, part 1: Theoretical development. Academic radiology, 13(11), 14101421.
 Kupinski, M. A., Clarkson, E., & Barrett, H. H. (2006). A Probabilistic Model for the MRMC Method, Part 2: Validation and Applications. Academic Radiology, 13(11), 14221430.More infoPMID: 17070461;PMCID: PMC2077079;Abstract: Rationale and Objectives: We have previously described a probabilistic model for the multiplereader, multiplecase paradigm for receiver operating characteristic analysis. When the figure of merit is the Wilcoxon statistic, this model returns a seventerm expansion for the variance of this statistic as a function of the numbers of cases and readers. This probabilistic model also provides expressions for the coefficients in the seventerm expansion in terms of expectations over the internal noise, readers, and cases. Finally, this probabilistic model sets bounds on both the overall variance of the Wilcoxon statistic and the individual coefficients. Materials and Methods: In this article, we will first validate the probabilistic model by comparing variances determined by direct computation of the expansion coefficients to empirical estimates of the variance using independent sampling. Validation of the probabilistic model will enable us to use the direct estimates of the expansion coefficients as a gold standard to compare other coefficientestimation techniques. Next, we develop a coefficientestimation technique that employs bootstrapping to estimate the Wilcoxon statistic variance for different numbers of readers and cases. We then employ constrained, leastsquares fitting techniques to estimate the expansion coefficients. The constraints used in this fitting are derived directly from the probabilistic model. Results: Using two different simulation studies, we show that the novel (and practical) bootstrapping/fitting technique returns estimates of the coefficients that are consistent with the gold standard. Conclusion: The results presented also serve to validate the seventerm expansion for the variance of the Wilcoxon statistic. © 2006 AUR.
 Kupinski, M. A., Clarkson, E., & Barrett, H. H. (2006). A probabilistic model for the MRMC method, part 2: Validation and applications. Academic radiology, 13(11), 14221430.
 Kupinski, M. A., Hoppin, J. W., Krasnow, J., Dahlberg, S., Leppo, J. A., King, M. A., Clarkson, E., & Barrett, H. H. (2006). Comparing cardiac ejection fraction estimation algorithms without a gold standard. Academic Radiology, 13(3), 329337.More infoPMID: 16488845;PMCID: PMC2464280;Abstract: Rationale and Objectives. Imaging and estimation of left ventricular function have major diagnostic and prognostic importance in patients with coronary artery disease. It is vital that the method used to estimate cardiac ejection fraction (EF) allows the observer to best perform this task. To measure taskbased performance, one must clearly define the task in question, the observer performing the task, and the patient population being imaged. In this report, the task is to accurately and precisely measure cardiac EF, and the observers are humanassisted computer algorithms that analyze the images and estimate cardiac EF. It is very difficult to measure the performance of an observer by using clinical data because estimation tasks typically lack a gold standard. A solution to this "nogoldstandard" problem recently was proposed, called regression without truth (RWT). Materials and Methods. Results of three different software packages used to analyze gated, cardiac, and nuclear medicine images, each of which uses a different algorithm to estimate a patient's cardiac EF, are compared. The three methods are the Emory method, Quantitative Gated SinglePhoton Emission Computed Tomographic method, and the WackersLiu Circumferential Quantification method. The same set of images is used as input to each of the three algorithms. Data were analyzed from the three different algorithms by using RWT to determine which produces the best estimates of cardiac EF in terms of accuracy and precision. Results and Discussion. In performing this study, three different consistency checks were developed to ensure that the RWT method is working properly. The Emory method of estimating EF slightly outperformed the other two methods. In addition, the RWT method passed all three consistency checks, garnering confidence in the method and its application to clinical data. © AUR, 2006.
 Kupinski, M. A., Hoppin, J. W., Krasnow, J., Dahlberg, S., Leppo, J. A., King, M. A., Clarkson, E., & Barrett, H. H. (2006). Comparing cardiac ejection fraction estimation algorithms without a gold standard. Academic radiology, 13(3), 329337.
 Park, S., Clarkson, E., Barrett, H. H., Kupinski, M. A., & Myers, K. J. (2006). Performance of a channelizedideal observer using LaguerreGauss channels for detecting a Gaussian signal at a known location in different lumpy backgrounds. Progress in Biomedical Optics and Imaging  Proceedings of SPIE, 6146.More infoAbstract: The Bayesian ideal observer gives a measure for image quality since it uses all available statistical information for a given image data. A channelizedideal observer (CIO), which reduces the dimensionality of integrals that need to be calculated for the ideal observer, has been introduced in the past. The goal of the CIO is to approximate the performance of the ideal observer in certain detection tasks. In this work, a CIO using LaguerreGauss (LG) channels is employed for detecting a rotationally symmetric Gaussian signal at a known location in the nonGaussian distributed lumpy background. The mean number of lumps in the lumpy background is varied to see the impact of image statistics on the performance of this CIO and a channelizedHotelling observer (CHO) using the same channels. The width parameter of LG channels is also varied to see its impact on observer performance. A Markovchain Monte Carlo (MCMC) method is employed to determine the performance of the CIO using large numbers of LG channels. Simulation results show that the CIO is a better observer than the CHO for the task. The results also indicate that the performance of the CIO approaches that of the ideal observer as the mean number of lumps in the lumpy background decreases. This implies that LG channels may be efficient for the CIO to approximate the performance of the ideal observer in tasks using nonGaussian distributed lumpy backgrounds.
 Sahu, A. K., Joshi, A., Kupinski, M. A., & SevickMuraca, E. M. (2006). Assessment of a fluorescenceenhanced optical imaging system using the Hotelling observer. Optics Express, 14(17), 76427660.More infoPMID: 19529133;PMCID: PMC2832206;Abstract: This study represents a first attempt to assess the detection capability of a fluorescenceenhanced optical imaging system as quantified by the Hotelling observer. The imaging system is simulated by the diffusion approximation of the timedependent radiative transfer equation, which describes near infrared (NIR) light propagation through a breast phantom of clinically relevant volume. Random structures in the background are introduced using a lumpyobject model as a representation of anatomical structure as well as nonuniform distribution of disease markers. The systematic errors and noise associated with the actual experimental conditions are incorporated into the simulated boundary measurements to acquire imaging data sets. A large number of imaging data sets is considered in order to perform Hotelling observer studies. We find that the signaltonoise ratio (SNR) of Hotelling observer (i) decreases as the strength of lumpy perturbations in the background increases, (ii) decreases as the target depth increases, and (iii) increases as excitation light leakage decreases, and reaches a maximum for filter optical density values of 5 or higher. © 2006 Optical Society of America.
 Sahu, A. K., Joshi, A., Kupinski, M. A., & SevickMuraca, E. M. (2006). Assessment of a fluorescenceenhanced optical imaging system using the Hotelling observer. Optics express, 14(17), 76427660.
 Barrett, H. H., Kupinski, M. A., & Clarkson, E. (2005). Probabilistic foundations of the MRMC method. Progress in Biomedical Optics and Imaging  Proceedings of SPIE, 5749, 2131.More infoAbstract: Current approaches to ROC analysis use the MRMC (multiplereader, multiplecase) paradigm in which several readers read each case and their ratings are used to construct an estimate of the area under the ROC curve or some other ROCrelated parameter. Standard practice is to decompose the parameter of interest according to a linear model into terms that depend in various ways on the readers, cases and modalities. It is assumed that the terms are statistically independent (or at least uncorrelated). Bootstrap methods are then used to estimate the variance of the estimate and the contributions from the individual terms in the assumed expansion. Though the methodological aspects of MRMC analysis have been studied in detail, the literature on the probabilistic basis of the individual terms is sparse. In particular, few papers state what probability law applies to each term and what underlying assumptions are needed for the assumed independence. This paper approaches the MRMC problem from a mechanistic perspective. For a single modality, three sources of randomness are included: the images, the reader skill and the reader uncertainty. The probability law on the parameter estimate is written in terms of three nested conditional probabilities, and random variables associated with this probability are referred to as triply stochastic. The triply stochastic probability is used to define the overall average of any ROC parameter as well as certain partial averages of utility in MRMC analysis. When this theory is applied to estimates of an ROC parameter for a single modality, it is shown that the variance of the estimate can be written as a sum of three terms, rather than the four that would be expected in MRMC analysis. The usual terms in MRMC expansions do not appear naturally in multiplystochastic theory. A rigorous MRMC expansion can be constructed by adding and subtracting partial averages to the parameter of interest in a tautological manner. In this approach the parameter is decomposed into a sum of four random uncorrelated, zeromean random variables, with each term clearly defined in terms of conditional probabilities. When the variance of the expansion is computed, however, numerous subtractions occur, and there is no apparent advantage to computing the variance term by term; the final result is the same as one gets from the triply stochastic decomposition, at least for the Wilcoxon estimator. No other nontrivial MRMC expansion appears to be possible.
 Gross, K., Kupinski, M. A., & Hesterman, J. Y. (2005). A fast model of a multiplepinhole SPECT imaging system. Progress in Biomedical Optics and Imaging  Proceedings of SPIE, 5749, 118127.More infoAbstract: The Center for GammaRay Imaging is developing a number of smallanimal SPECT imaging systems. These systems consist of multiple stationary detectors, each of which has its own multiplepinhole collimator. The location of the pinhole plates (i.e., magnification), the number of pinholes within each plate, as well the pinhole locations are all adjustable. The performance of the Bayesian ideal observer sets the upper limit on task performance and can be used to optimize imaging hardware, such as pinhole configurations. Markovchain Monte Carlo techniques have been developed to compute the ideal observer but require complete knowledge of the statistics of both the imaging system (such as the noise) and the class of random objects being imaged, in addition to an accurate forward model connecting the object to the image. Ideal observer computations using Monte Carlo techniques are burdensome because the forward model must be simulated millions of times for each imaging system. We present an efficient technique for computing the Bayesian ideal observer for multiplepinhole, smallanimal SPECT systems that accounts for both the finitesize of the pinholes and the stochastic nature of the objects being imaged. This technique relies on an efficient, radiometrically correct forward model that maps an object to an image in less than 20 milliseconds. An analysis of the error of the forward model, as well as the results of a ROC study using the ideal observer test statistic is presented.
 Hesterman, J. Y., Kupinski, M. A., Furenlid, L. R., & Wilson, D. W. (2005). Experimental taskbased optimization of a fourcamera variablepinhole smallanimal SPECT system. Progress in Biomedical Optics and Imaging  Proceedings of SPIE, 5749, 300309.More infoAbstract: We have previously utilized lumpy object models and simulated imaging systems in conjunction with the ideal observer to compute figures of merit for hardware optimization. In this paper, we describe the development of methods and phantoms necessary to validate or experimentally carry out these optimizations. Our study was conducted on a fourcamera smallanimal SPECT system that employs interchangeable pinhole plates to operate under a variety of pinhole configurations and magnifications (representing optimizable system parameters). We developed a smallanimal phantom capable of producing random backgrounds for each image sequence. The task chosen for the study was the detection of a 2mm diameter sphere within the phantomgenerated random background. A total of 138 projection images were used, half of which included the signal. As our observer, we employed the channelized Hotelling observer (CHO) with LaguerreGauss channels. The signaltonoise (SNR) of this observer was used to compare different system configurations. Results indicate agreement between experimental and simulated data with higher detectability rates found for multiplecamera, multiplepinhole, and highmagnification systems, although it was found that mixtures of magnifications often outperform systems employing a single magnification. This work will serve as a basis for future studies pertaining to system hardware optimization.
 Kupinski, M. A., & Clarkson, E. (2005). Extending the channelized Hotelling observer to account for signal uncertainty and estimation tasks. Progress in Biomedical Optics and Imaging  Proceedings of SPIE, 5749, 183190.More infoAbstract: In medicine, images are taken so that specific tasks can be performed. Thus, any measure of image quality must account for the task the images are to be used for and the observer performing the task. Performing taskbased optimizations using human observers is generally difficult, time consuming, expensive and, in the case of hardware optimizations, not necessarily ideal. Model observers have been successfully used in place of human observers. The channelized Hotelling observer is one such model observer. Depending on the choice of channels, the channelized Hotelling observer can be used to either predict humanobserver performance or as an ideal observer. This paper will focus on the use of the channelized Hotelling observer as an approximation of the ideal linear observer. Laguerre Gauss channels have proven useful for idealobserver computations, but these channels are somewhat limited because they require the signal to be known exactly both in terms of location and shape. In fact, the Laguerre Gauss channels require the signal to be radially symmetric. We have devised a new method of determining efficient channels that does not require the signal to be symmetric and can even account for signal variability. This method can even be used for linear estimation tasks. We have compared the performances of the channelized Hotelling observer using both this new set of channels and the Laguerre Gauss channels for a signalknownexactly detection task, and found that they correlate.
 Park, S., Clarkson, E., Kupinski, M. A., & Barrett, H. H. (2005). Efficiency of human and model observers for signaldetection tasks in nonGaussian distributed lumpy backgrounds. Progress in Biomedical Optics and Imaging  Proceedings of SPIE, 5749, 138149.More infoAbstract: Efficiencies of the human observer and channelizedHotelling observers (CHOs) relative to the ideal observer for signaldetection tasks are discussed. A CHO using LaguerreGauss channels, which we call an efficient CHO (eCHO), and a CHO adding a scanning scheme to the eCHO to include signallocation uncertainty, which we call a scanning eCHO (seCHO), are considered. Both signalknownexactly (SKE) tasks and signalknownstatistically (SKS) tasks are considered. Signal location is uncertain for the SKS tasks, and lumpy backgrounds are used for background uncertainty in both the tasks. Markovchain Monte Carlo methods are employed to determine idealobserver performance on the detection tasks. Psychophysical studies are conducted to compute humanobserver performance on the same tasks. A maximumlikelihood estimation method is employed to fit smooth psychometric curves with observer performance measurements. Efficiency is computed as the squared ratio of the detectabilities of the observer of interest to a standard observer. Depending on image statistics, the ideal observer or the Hotelling observer is used as the standard observer. The results show that the eCHO performs poorly in detecting signals with location uncertainty and the seCHO performs only slightly better while the ideal observer outperforms the human observer and CHOs for both the tasks. Human efficiencies are approximately less than 2.5% and 41%, respectively, for the SKE and SKS tasks, where the gray levels of the lumpy background are nonGaussian distributed. These results also imply that human observers are not affected by signallocation uncertainty as much as the ideal observer. However, for the SKE tasks using Gaussiandistributed lumpy backgrounds, the human efficiency ranges between 28% and 42%. Three different simplified pinhole imaging systems are simulated and the humans and the model observers rank the systems in the same order for both the tasks.
 Park, S., Clarkson, E., Kupinski, M. A., & Barrett, H. H. (2005). Efficiency of the human observer detecting random signals in random backgrounds. JOSA A, 22(1), 316.
 Park, S., Clarkson, E., Kupinski, M. A., & Barrett, H. H. (2005). Efficiency of the human observer detecting random signals in random backgrounds. Journal of the Optical Society of America A: Optics and Image Science, and Vision, 22(1), 316.More infoPMID: 15669610;PMCID: PMC2464287;Abstract: The efficiencies of the human observer and the channelizedHotelling observer relative to the ideal observer for signaldetection tasks are discussed. Both signalknownexactly (SKE) tasks and signalknownstatistically (SKS) tasks are considered. Signal location is uncertain for the SKS tasks, and lumpy backgrounds are used for background uncertainty in both cases. Markov chain Monte Carlo methods are employed to determine idealobserver performance on the detection tasks. Psychophysical studies are conducted to compute humanobserver performance on the same tasks. Efficiency is computed as the squared ratio of the detectabilities of the observer of interest to the ideal observer. Human efficiencies are approximately 2.1% and 24%. respectively, for the SKE and SKS tasks. The results imply that human observers are not affected as much as the ideal observer by signallocation uncertainty even though the ideal observer outperforms the human observer for both tasks. Three different simplified pinhole imaging systems are simulated, and the humans and the model observers rank the systems in the same order for both the SKE and the SKS tasks. © 2005 Optical Society of America.
 Edwards, D. C., Metz, C. E., & Kupinski, M. A. (2004). Ideal observers and optimal ROC hypersurfaces in Nclass classification. IEEE Transactions on Medical Imaging, 23(7), 891895.More infoPMID: 15250641;PMCID: PMC2464283;Abstract: The likelihood ratio, or ideal observer, decision rule is known to be optimal for twoclass classification tasks in the sense that it maximizes expected utility (or, equivalently, minimizes the Bayes risk). Furthermore, using this decision rule yields a receiver operating characteristic (ROC) curve which is never above the ROC curve produced using any other decision rule, provided the observer's misclassification rate with respect to one of the two classes is chosen as the dependent variable for the curve (i.e., an "inversion" of the more common formulation in which the observer's truepositive fraction is plotted against its falsepositive fraction). It is also known that for a decision task requiring classification of observations into N classes, optimal performance in the expected utility sense is obtained using a set of N  1 likelihood ratios as decision variables. In the Nclass extension of ROC analysis, the ideal observer performance is describable in terms of an (N2  N  1)parameter hypersurface in an (N2  N)dimensional probability space. We show that the result for two classes holds in this case as well, namely that the ROC hypersurface obtained using the ideal observer decision rule is never above the ROC hypersurface obtained using any other decision rule (where in our formulation performance is given exclusively with respect to betweenclass error rates rather than withinclass sensitivities).
 Edwards, D. C., Metz, C. E., & Kupinski, M. A. (2004). Ideal observers and optimal ROC hypersurfaces in Nclass classification. Medical Imaging, IEEE Transactions on, 23(7), 891895.
 Kupinski, M. A., & Clarkson, E. (2004). Imagequality assessment in optical tomography. 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano, 2, 14711474.More infoAbstract: Modern medical imaging systems often rely on complicated hardware and sophisticated algorithms to produce useful digital images. It is essential that the imaging hardware and any reconstruction algorithms used are optimized, enabling radiologists to make the best decisions and quantify a patient's health status. Optimization of the hardware often entails determining the physical design of the system, such as the the locations of detectors in optical tomography or the design of the collimator in SPECT systems. For software or reconstruction algorithm optimization one is often determining the values of regularization parameters or the number of iterations in an iterative algorithm. In this paper, we present an overview of many approaches to measuring task performance as a means to optimize imaging systems and algorithms. Much of the work in this area has taken place in the areas of nuclearmedicine and xray imaging. The purpose of this paper is to present some of the taskbased measures of image quality that are directly applicable to optical tomography. © 2004 IEEE.
 Park, S., Kupinski, M. A., Clarkson, E., & Barrett, H. H. (2004). Efficient channels for the ideal observer. Progress in Biomedical Optics and Imaging  Proceedings of SPIE, 5(26), 1221.More infoAbstract: For a signaldetection task, the Bayesian ideal observer is optimal among all observers because it incorporates all the statistical information of the raw data from an imaging system. The ideal observer test statistic, the likelihood ratio, is difficult to compute when uncertainties are present in backgrounds and signals. In this work, we propose a new approximation technique to estimate the likelihood ratio. This technique is a dimensionalityreduction scheme we will call the channelizedideal observer (CIO). We can reduce the highdimensional integrals of the ideal observer to the lowdimensional integrals of the CIO by applying a set of channels to the data. Lumpy backgrounds and circularly symmetric Gaussian signals are used for simulations studies. LaguerreGaussian (LG) channels have been shown to be useful for approximating ideal linear observers with these backgrounds and signals. For this reason, we choose to use LG channels for our data. The concept of efficient channels is introduced to closely approximate idealobserver performance with the CIO for signalknownexactly (SKE) detection tasks. Preliminary results using one to three LG channels show that the performance of the CIO is better than the channelizedHotelling observer for the SKE detection tasks.
 Clarkson, E., Kupinski, M. A., & Hoppin, J. W. (2003). Assessing the accuracy of estimates of the likelihood ratio. Proceedings of SPIE  The International Society for Optical Engineering, 5034, 135143.More infoAbstract: There are many methods to estimate, from ensembles of signalpresent and signalabsent images, the area under the receiver operating characteristic curve for an observer in a detection task. For the ideal observer on realistic detection tasks, all of these methods are time consuming due to the difficulty in calculating the idealobserver test statistic. There are relations, in the form of equations and inequalities, that can be used to check these estimates by comparing them to other quantities that can also be estimated from the ensembles. This is especially useful for evaluating these estimates for any possible bias due to small sample sizes or errors in the calculation of the likelihood ratio. This idea is demonstrated with a simulation of an idealized single photon emission detector array viewing a possible signal in a twodimensional lumpy activity distribution.
 Gross, K., Kupinski, M. A., Peterson, T., & Clarkson, E. (2003). Optimizing a multiplepinhole spect system using the ideal observer. Proceedings of SPIE  The International Society for Optical Engineering, 5034, 314322.More infoAbstract: In a pinhole imaging system, multiple pinholes are potentially beneficial since more radiation will arrive in the detector plane. However, the various images produced by each pinhole may multiplex (overlap), possibly decreasing image quality. In this work we develop the framework for comparing various pinhole configurations using idealobserver performance as a figure of merit. We compute the idealobserver test statistic, the likelihood ratio, using a statistical method known as MarkovChain Monte Carlo. For different imaging systems, we estimate the likelihood ratio for many realizations of noisy image data both with and without a signal present. For each imaging system, the area under the ROC curve provides a meaningful figure of merit for hardware comparison. In this work we compare different pinhole configurations using a threedimensional lumpy object model, a known signal (SKE), and simulated pinhole imaging systems. The results of our work will eventually serve as a basis for a design of highresolution pinhole SPECT systems.
 Hoppin, J. W., Kupinski, M. A., Wilson, D. W., Peterson, T., Gershman, B., Kastis, G., Clarkson, E., Furenlid, L., & Barrett, H. H. (2003). Evaluating estimation techniques in medical imaging without a gold standard: Experimental validation. Proceedings of SPIE  The International Society for Optical Engineering, 5034, 230237.More infoAbstract: Imaging is often used for the purpose of estimating the value of some parameter of interest. For example, a cardiologist may measure the ejection fraction (EF) of the heart to quantify how much blood is being pumped out of the heart on each stroke. In clinical practice, however, it is difficult to evaluate an estimation method because the gold standard is not known, e.g., a cardiologist does not know the true EF of a patient. An estimation method is typically evaluated by plotting its results against the results of another (more accepted) estimation method. This approach results in the use of one set of estimates as the pseudogold standard. We have developed a maximumlikelihood approach for comparing different estimation methods to the gold standard without the use of the gold standard. In previous works we have displayed the results of numerous simulation studies indicating the method can precisely and accurately estimate the parameters of a regression line without a gold standard, i.e., without the xaxis. In an attempt to further validate our method we have designed an experiment performing volume estimation using a physical phantom and two imaging systems (SPECT,CT).
 Kupinski, M. A. (2003). Computing in optics. Computing in Science and Engineering, 5(6), 1314.
 Kupinski, M. A. (2003). Guest Editor's Introduction: Computing in Optics. Computing in Science \& Engineering, 5(6), 001314.
 Kupinski, M. A., Clarkson, E., Gross, K., & Hoppin, J. W. (2003). Optimizing imaging hardware for estimation tasks. Proceedings of SPIE  The International Society for Optical Engineering, 5034, 309313.More infoAbstract: Medical imaging is often performed for the purpose of estimating a clinically relevant parameter. For example, cardiologists are interested in the cardiac ejection fraction, the fraction of blood pumped out of the left ventricle at the end of each heart cycle. Even when the primary task of the imaging system is tumor detection, physicians frequently want to estimate parameters of the tumor, e.g. size and location. For signaldetection tasks, we advocate that the performance of an ideal observer be employed as the figure of merit for optimizing medical imaging hardware. We have examined the use of the minimum variance of the ideal, unbiased estimator as a figure of merit for hardware optimization. The minimum variance of the ideal, unbiased estimator can be calculated using the Fisher information matrix. To account for both image noise and object variability, we used a statistical method known as Markovchain Monte Carlo. We employed a lumpy object model and simulated imaging systems to compute our figures of merit. We have demonstrated the use of this method in comparing imaging systems for estimation tasks.
 Kupinski, M. A., Clarkson, E., Hoppin, J. W., Chen, L., & Barrett, H. H. (2003). Experimental determination of object statistics from noisy images. JOSA A, 20(3), 421429.
 Kupinski, M. A., Clarkson, E., Hoppin, J. W., Chen, L., & Barrett, H. H. (2003). Experimental determination of object statistics from noisy images. Journal of the Optical Society of America A: Optics and Image Science, and Vision, 20(3), 421429.More infoPMID: 12630828;PMCID: PMC1785324;Abstract: Modern imaging systems rely on complicated hardware and sophisticated imageprocessing methods to produce images. Owing to this complexity in the imaging chain, there are numerous variables in both the hardware and the software that need to be determined. We advocate a taskbased approach to measuring and optimizing image quality in which one analyzes the ability of an observer to perform a task. Ideally, a taskbased measure of image quality would account for all sources of variation in the imaging system, including object variability. Often, researchers ignore object variability even though it is known to have a large effect on task performance. The more accurate the statistical description of the objects, the more believable the taskbased results will be. We have developed methods to fit statistical models of objects, using only noisy image data and a wellcharacterized imaging system. The results of these techniques could eventually be used to optimize both the hardware and the software components of imaging systems. © 2003 Optical Society of America.
 Kupinski, M. A., Hoppin, J. W., Clarkson, E., & Barrett, H. H. (2003). Idealobserver computation in medical imaging with use of Markovchain Monte Carlo techniques. JOSA A, 20(3), 430438.
 Kupinski, M. A., Hoppin, J. W., Clarkson, E., & Barrett, H. H. (2003). Idealobserver computation in medical imaging with use of Markovchain Monte Carlo techniques. Journal of the Optical Society of America A: Optics and Image Science, and Vision, 20(3), 430438.More infoPMID: 12630829;PMCID: PMC2464282;Abstract: The ideal observer sets an upper limit on the performance of an observer on a detection or classification task. The performance of the ideal observer can be used to optimize hardware components of imaging systems and also to determine another observer's relative performance in comparison with the best possible observer. The ideal observer employs complete knowledge of the statistics of the imaging system, including the noise and object variability. Thus computing the ideal observer for images (largedimensional vectors) is burdensome without severely restricting the randomness in the imaging system, e.g., assuming a flat object. We present a method for computing the idealobserver test statistic and performance by using Markovchain Monte Carlo techniques when we have a wellcharacterized imaging system, knowledge of the noise statistics, and a stochastic object model. We demonstrate the method by comparing three different parallelhole collimator imaging systems in simulation. © 2003 Optical Society of America.
 Park, S., Kupinski, M. A., Clarkson, E., & Barrett, H. H. (2003). Idealobserver performance under signal and background uncertainty. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2732, 342353.More infoAbstract: We use the performance of the Bayesian ideal observer as a figure of merit for hardware optimization because this observer makes optimal use of signaldetection information. Due to the high dimensionality of certain integrals that need to be evaluated, it is difficult to compute the ideal observer test statistic, the likelihood ratio, when background variability is taken into account. Methods have been developed in our laboratory for performing this computation for fixed signals in random backgrounds. In this work, we extend these computational methods to compute the likelihood ratio in the case where both the backgrounds and the signals are random with known statistical properties. We are able to write the likelihood ratio as an integral over possible backgrounds and signals, and we have developed Markovchain Monte Carlo (MCMC) techniques to estimate these highdimensional integrals. We can use these results to quantify the degradation of the idealobserver performance when signal uncertainties are present in addition to the randomness of the backgrounds. For background uncertainty, we use lumpy backgrounds. We present the performance of the ideal observer under various signaluncertainty paradigms with different parameters of simulated parallelhole collimator imaging systems. We are interested in any change in the rankings between different imaging systems under signal and background uncertainty compared to the backgrounduncertainty case. We also compare psychophysical studies to the performance of the ideal observer. © SpringerVerlag Berlin Heidelberg 2003.
 Park, S., Kupinski, M. A., Clarkson, E., & Barrett, H. H. (2003). Idealobserver performance under signal and background uncertainty.. Inf Process Med Imaging, 18, 342353.More infoPMID: 15344470;Abstract: We use the performance of the Bayesian ideal observer as a figure of merit for hardware optimization because this observer makes optimal use of signaldetection information. Due to the high dimensionality of certain integrals that need to be evaluated, it is difficult to compute the ideal observer test statistic, the likelihood ratio, when background variability is taken into account. Methods have been developed in our laboratory for performing this computation for fixed signals in random backgrounds. In this work, we extend these computational methods to compute the likelihood ratio in the case where both the backgrounds and the signals are random with known statistical properties. We are able to write the likelihood ratio as an integral over possible backgrounds and signals, and we have developed Markovchain Monte Carlo (MCMC) techniques to estimate these highdimensional integrals. We can use these results to quantify the degradation of the idealobserver performance when signal uncertainties are present in addition to the randomness of the backgrounds. For background uncertainty, we use lumpy backgrounds. We present the performance of the ideal observer under various signaluncertainty paradigms with different parameters of simulated parallelhole collimator imaging systems. We are interested in any change in the rankings between different imaging systems under signal and background uncertainty compared to the backgrounduncertainty case. We also compare psychophysical studies to the performance of the ideal observer.
 Clarkson, E., Kupinski, M. A., & Barrett, H. H. (2002). Transformation of characteristic functionals through imaging systems. Optics Express, 10(13), 536539.More infoPMID: 19436394;PMCID: PMC3143023;Abstract: We describe how to transfer the characteristic functional of an object model through a noisy, discrete imaging system to arrive at the characteristic function of the images. Our method can also incorporate linear postprocessing of the images. © 2002 Optical Society of America.
 Clarkson, E., Kupinski, M., & Barrett, H. (2002). Transformation of characteristic functionals through imaging systems. Optics express, 10(13), 536539.
 Drukker, K., Giger, M. L., Horsch, K., Kupinski, M. A., Vyborny, C. J., & Mendelson, E. B. (2002). Computerized lesion detection on breast ultrasound. Medical Physics, 29(7), 14381446.
 Drukker, K., Giger, M. L., Horsch, K., Kupinski, M. A., Vyborny, C. J., & Mendelson, E. B. (2002). Computerized lesion detection on breast ultrasound. Medical Physics, 29(7), 14381446.More infoPMID: 12148724;Abstract: We investigated the use of a radial gradient index (RGI) filtering technique to automatically detect lesions on breast ultrasound. After initial RGI filtering, a sensitivity of 87% at 0.76 falsepositive detections per image was obtained on a database of 400 patients (757 images). Next, lesion candidates were segmented from the background by maximizing an average radial gradient (ARD) index for regions grown from the detected points. At an overlap of 0.4 with a radiologist lesion outline, 75% of the lesions were correctly detected. Subsequently, round robin analysis was used to assess the quality of the classification of lesion candidates into actual lesions and falsepositives by a Bayesian neural network. The round robin analysis yielded an Az value of 0.84, and an overall performance by case of 94% sensitivity at 0.48 falsepositives per image. Use of computerized analysis of breast sonograms may ultimately facilitate the use of sonography in breast cancer screening programs. © 2002 American Association of Physicists in Medicine.
 Edwards, D. C., Kupinski, M. A., Metz, C. E., & Nishikawa, R. M. (2002). Maximum likelihood fitting of FROC curves under an initialdetectionandcandidateanalysis model. Medical Physics, 29(12), 28612870.More infoPMID: 12512721;Abstract: We have developed a model for FROC curve fitting that relates the observer's FROC performance not to the ROC performance that would be obtained if the observer's responses were scored on a per image basis, but rather to a hypothesized ROC performance that the observer would obtain in the task of classifying a set of "candidate detections" as positive or negative. We adopt the assumptions of the Bunch FROC model, namely that the observer's detections are all mutually independent, as well as assumptions qualitatively similar to, but different in nature from, those made by Chakraborty in his AFROC scoring methodology. Under the assumptions of our model, we show that the observer's FROC performance is a linearly scaled version of the candidate analysis ROC curve, where the scaling factors are just given by the FROC operating point coordinates for detecting initial candidates. Further, we show that the likelihood function of the model parameters given observational data takes on a simple form, and we develop a maximum likelihood method for fitting a FROC curve to this data. FROC and AFROC curves are produced for computer vision observer datasets and compared with the results of the AFROC scoring method. Although developed primarily with computer vision schemes in mind, we hope that the methodology presented here will prove worthy of further study in other applications as well. © 2002 American Association of Physicists in Medicine.
 Edwards, D. C., Kupinski, M. A., Metz, C. E., & Nishikawa, R. M. (2002). Maximum likelihood fitting of FROC curves under an initialdetectionandcandidateanalysis model. Medical physics, 29(12), 28612870.
 Hoppin, J. W., Kupinski, M. A., Kastis, G. A., Clarkson, E., & Barrett, H. H. (2002). Objective comparison of quantitative imaging modalities without the use of a gold standard. IEEE Transactions on Medical Imaging, 21(5), 441449.More infoPMID: 12071615;PMCID: PMC3150581;Abstract: Imaging is often used for the purpose of estimating the value of some parameter of interest. For example, a cardiologist may measure the ejection fraction (EF) of the heart in order to know how much blood is being pumped out of the heart on each stroke. In clinical practice, however, it is difficult to evaluate an estimation method because the gold standard is not known, e.g., a cardiologist does not know the true EF of a patient. Thus, researchers have often evaluated an estimation method by plotting its results against the results of another (more accepted) estimation method, which amounts to using one set of estimates as the pseudogold standard. In this paper, we present a maximumlikelihood approach for evaluating and comparing different estimation methods without the use of a gold standard with specific emphasis on the problem of evaluating EF estimation methods. Results of numerous simulation studies will be presented and indicate that the method can precisely and accurately estimate the parameters of a regression line without a gold standard, i.e., without the x axis.
 Hoppin, J. W., Kupinski, M. A., Kastis, G. A., Clarkson, E., & Barrett, H. H. (2002). Objective comparison of quantitative imaging modalities without the use of a gold standard. Medical Imaging, IEEE Transactions on, 21(5), 441449.
 Kupinski, M. A., Clarkson, E., & Barrett, H. H. (2002). Matching statistical object models to real images. Proceedings of SPIE  The International Society for Optical Engineering, 4686, 3742.More infoAbstract: We advocate a taskbased approach to measuring and optimizing image quality; that is, optimize imaging systems based on the performance of a particular observer performing a specific task. This type of analysis can require numerous images and is, thus, infeasible with real patients. Researchers are forced to employ statistical models from which they can produce as many images as required. We have developed methods to accurately fit statistical models of continuous objects to real images. The fitted models can be used for hardware optimizations as well as imageprocessing optimizations. We have employed a continuous lumpy object model in this research and found that our method can accurately determine model parameters in simulation.
 Kupinski, M. A., Hoppin, J. W., Clarkson, E., Barrett, H. H., & Kastis, G. A. (2002). Estimation in medical imaging without a gold standard. Academic Radiology, 9(3), 290297.More infoPMID: 11887945;PMCID: PMC3143018;Abstract: Rationale and Objectives. In medical imaging, physicians often estimate a parameter of interest (eg, cardiac ejection fraction) for a patient to assist in establishing a diagnosis. Many different estimation methods may exist, but rarely can one be considered a gold standard. Therefore, evaluation and comparison of different estimation methods are difficult. The purpose of this study was to examine a method of evaluating different estimation methods without use of a gold standard. Materials and Methods. This method is equivalent to fitting regression lines without the x axis. To use this method, multiple estimates of the clinical parameter of interest for each patient of a given population were needed. The authors assumed the statistical distribution for the true values of the clinical parameter of interest was a member of a given family of parameterized distributions. Furthermore, they assumed a statistical model relating the clinical parameter to the estimates of its value. Using these assumptions and observed data, they estimated the model parameters and the parameters characterizing the distribution of the clinical parameter. Results. The authors applied the method to simulated cardiac ejection fraction data with varying numbers of patients, numbers of modalities, and levels of noise. They also tested the method on both linear and nonlinear models and characterized the performance of this method compared to that of conventional regression analysis by using xaxis information. Results indicate that the method follows trends similar to that of conventional regression analysis as patients and noise vary, although conventional regression analysis outperforms the method presented because it uses the gold standard which the authors assume is unavailable. Conclusion. The method accurately estimates model parameters. These estimates can be used to rank the systems for a given estimation task. © AUR, 2002.
 Kupinski, M. A., Hoppin, J. W., Clarkson, E., Barrett, H. H., & Kastis, G. A. (2002). Estimation in medical imaging without a gold standard. Academic radiology, 9(3), 290297.
 Liu, Z., Kastis, G. A., Stevenson, G. D., Barrett, H. H., Furenlid, L. R., Kupinski, M. A., Patton, D. D., & Wilson, D. W. (2002). BASIC SCIENCE INVESTIGATIONSQuantitative Analysis of Acute Myocardial lnfarct in Rat Hearts with IschemiaReperfusion Using a HighResolution Stationary SPECT System. Journal of Nuclear Medicine, 43(7), 933939.
 Liu, Z., Kastis, G. A., Stevenson, G. D., Barrett, H. H., Furenlid, L. R., Kupinski, M. A., Patton, D. D., & Wilson, D. W. (2002). Quantitative analysis of acute myocardial infarct in rat hearts with ischemiareperfusion using a highresolution stationary SPECT system. Journal of Nuclear Medicine, 43(7), 933939.
 Liu, Z., Kastis, G. A., Stevenson, G. D., Barrett, H. H., Furenlid, L. R., Kupinski, M. A., Patton, D. D., & Wilson, D. W. (2002). Quantitative analysis of acute myocardial infarct in rat hearts with ischemiareperfusion using a highresolution stationary SPECT system. Journal of Nuclear Medicine, 43(7), 933939.More infoPMID: 12097466;PMCID: PMC3062997;Abstract: The purpose of this study was to develop an in vivo imaging protocol for a highresolution stationary SPECT system, called FASTSPECT, in a rat heart model of ischemiareperfusion (IR) and to compare 99mTcsestamibi imaging and triphenyltetrazolium chloride (TTC) staining for reliability and accuracy in the measurement of myocardial infarcts. Methods: FASTSPECT consists of 24 modular cameras and a 24pinhole aperture with 1.5mm spatial resolution and 13.3 cps/μCi (0.359 cps/kBq) sensitivity. The IR heart model was created by ligating the left coronary artery for 90 min and then releasing the ligature for 30 min. Two hours after 99mTcsestamibi injection (510 mCi [185370 MBq]), images were acquired for 510 min for 5 control rats and 11 IR rats. The hearts were excised, and the left ventricle was sectioned into 4 slices for TTC staining. Results: Left and right ventricular myocardium in control rats was shown clearly, with uniform 99mTcsestamibi distribution and 100% TTC staining for viable myocardium. Nine of 11 rats with IR survived throughout imaging and exhibited 50.8% ± 2.7% ischemic area and 37.9% ± 3.9% infarct in the left ventricle on TTC staining. The infarct size measured by FASTSPECT imaging was 37.6% ± 3.6%, which correlated significantly with that measured by TTC staining (r = 0.974; P < 0.01). Conclusion: The results confirmed the accuracy of FASTSPECT imaging for measurement of acute myocardial infarcts in rat hearts. Application of FASTSPECT imaging in small animals may be feasible for investigating myocardial IR injury and the effects of revascularization.
 Edwards, D. C., Papaioannou, J., Jiang, Y., Kupinski, M. A., & Nishikawa, R. M. (2001). Eliminating falsepositive microcalcification clusters in a mammography CAD scheme using a Bayesian neural network. Proceedings of SPIE  The International Society for Optical Engineering, 4322(3), 19541960.More infoAbstract: We have applied a Bayesian neural network (BNN) to the task of distinguishing between truepositive (TP) and falsepositive (FP) detected clusters in a computeraided diagnosis (CAD) scheme for detecting clustered microcalcifications in mammograms. Because BNNs can approximate ideal observer decision functions given sufficient training data, this approach should have better performance than our previous FP cluster elimination methods. Eight clusterbased features were extracted from the TP and FP clusters detected by the scheme in a training dataset of 39 mammograms. This set of features was used to train a BNN with eight input nodes, five hidden nodes, and one output node. The trained BNN was tested on the TP and FP clusters detected by our scheme in an independent testing set of 50 mammograms. The BNN output was analyzed using ROC and FROC analysis. The detection scheme with the BNN for FP cluster elimination had substantially better cluster sensitivity at low FP rates (below 0.8 FP clusters per image) than the original detection scheme without the BNN. Our preliminary research shows that a BNN can improve the performance of our scheme for detecting clusters of microcalcifications.
 Edwards, D., Papaioannou, J., Jiang, Y., Kupinski, M., & Nishikawa, R. (2001). Eliminating falsepositive microcalcification clusters in a mammography CAD scheme using a Bayesian neural network [4322226]. PROCEEDINGSSPIE THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING, 19541960.
 Kupinski, M. A., Edwards, D. C., Giger, M. L., & Metz, C. E. (2001). Ideal observer approximation using Bayesian classification neural networks. IEEE Transactions on Medical Imaging, 20(9), 886899.More infoPMID: 11585206;Abstract: It is well understood that the optimal classification decision variable is the likelihood ratio or any monotonic transformation of the likelihood ratio. An automated classifier which maps from an input space to one of the likelihood ratio family of decision variables is an optimal classifier or "ideal observer" Artificial neural networks (ANNs) are frequently used as classifiers for many problems. In the limit of large training sample sizes, an ANN approximates a mapping function which is a monotonic transformation of the likelihood ratio, i.e., it estimates an ideal observer decision variable. A principal disadvantage of conventional ANNs is the potential overparameterization of the mapping function which results in a poor approximation of an optimal mapping function for smaller training samples. Recently, Bayesian methods have been applied to ANNs in order to regularize training to improve the robustness of the classifier. The goal of training a Bayesian ANN with finite sample sizes is, as with unlimited data, to approximate the ideal observer. We have evaluated the accuracy of Bayesian ANN models of ideal observer decision variables as a function of the number of hidden units used, the signaltonoise ratio of the data and the number of features or dimensionality of the data. We show that when enough training data are present, excess hidden units do not substantially degrade the accuracy of Bayesian ANNs. However, the minimum number of hidden units required to best model the optimal mapping function varies with the complexity of the data.
 Kupinski, M. A., Edwards, D. C., Giger, M. L., & Metz, C. E. (2001). Ideal observer approximation using Bayesian classification neural networks. Medical Imaging, IEEE Transactions on, 20(9), 886899.
 Edwards, D. C., Kupinski, M. A., Nishikawa, R. M., & Metz, C. E. (2000). Estimation of linear observer templates in the presence of multipeaked Gaussian noise through 2AFC experiments. Proceedings of SPIE  The International Society for Optical Engineering, 3981, 8596.More infoAbstract: We extend a method for linear template estimation developed by Abbey et al. which demonstrated that a linear observer template can be estimated effectively through a twoalternative forced choice (2AFC) experiment, assuming the noise in the images is Gaussian, or multivariate normal (MVN). We relax this assumption, allowing the noise in the images to be drawn from a weighted sum of MVN distributions, which we call a multipeaked MVN (MPMVN) distribution. Our motivation is that more complicated probability density functions might be approximated in general by such MPMVN distributions. Our extension of Abbey et al.'s method requires us to impose the additional constraint that the covariance matrices of the component peaks of the signalpresent noise distribution all be equal, and that the covariance matrices of the component peaks of the signalabsent noise distribution all be equal (but different in general from the signalpresent covariance matrices). Preliminary research shows that our generalized method is capable of producing unbiased estimates of linear observer templates in the presence of MPMVN noise under the stated assumptions. We believe this extension represents a next step toward the general treatment of arbitrary image noise distributions.
 Edwards, D., Kupinski, M., Nagel, R., Nishikawa, R., & Papaioannou, J. (2000). Using a Bayesian neural network to optimally eliminate falsepositive microcalcification detections in a CAD scheme. Digital Mammography, Medical Physics Publishing, Madison, 168173.
 Esthappan, J., Kupinski, M. A., Lan, L., & Hoffmann, K. R. (2000). A method for the determination of the 3D orientations and positions of catheters from singleplane xray images. Annual International Conference of the IEEE Engineering in Medicine and Biology  Proceedings, 3, 20292032.More infoAbstract: The threedimensional (3D) orientation and position of an object, i.e., a configuration of points, can be determined by use of the single projection technique (SPT) from a single projection image, given the relative 3D positions of the points and initial estimates of the orientation and position. The accuracy of the SPT for the case of Lshaped catheters was evaluated in simulation studies. Catheter models were generated, oriented and positioned, and then projected onto an image plane to generate projection images. Gaussiandistributed noise was added to the image positions. The 3D orientations and positions of the catheters were determined using the SPT, which iteratively aligns the model points with their respective image positions. Studies indicate that the orientation and position of a catheter of diameter 0.18 cm can be determined to within 1.6° and 0.8 cm, respectively. These results are comparable to those obtained with a Jshaped catheter indicating that the technique is generally applicable independent of catheter shape. Studies indicate that the SPT may provide the basis for the automated determination of the orientations of catheters in vivo from singleplane projection images. This automated method may facilitate interventional procedures by eliminating the need for imaging the vasculature at various angulations of the gantry, and may, thereby, reduce procedure times, complications, and radiation dose. In the future, the information provided by the SPT may be employed by 3D vessel reconstruction techniques to extend conventional roadmapping techniques from 2D to 3D.
 Giger, M. L., Huo, Z., Kupinski, M. A., & Vyborny, C. J. (2000). Computeraided diagnosis in mammography. Handbook of medical imaging, 2, 9151004.
 Kupinski, M. A., Anastasio, M. A., & Giger, M. L. (2000). Multiobjective genetic optimization of diagnostic classifiers used in the computerized detection of mass lesions in mammography. Proceedings of SPIE  The International Society for Optical Engineering, 3979, I/.More infoAbstract: We have recently proposed and developed a multiobjective approach to training classification systems. In this approach, the objectives, i.e., the sensitivity and specificity, of a classifier are simultaneously optimized, resulting in a series of solutions that are equivalent in the absence of any a priori knowledge regarding the relative merits of the two objectives. These solutions form a receiver operating characteristic (ROC) curve that is, theoretically, the best possible ROC curve that can be obtained using the given classifier and given training dataset. We have applied this technique to the optimization of classifiers for the computerized detection of mass lesions in digitized mammograms. Comparisons will be made between the results obtained using the multiobjective approach and results obtained using more conventional approaches. We employed a database of 60 consecutive, nonpalpable mass lesion cases. Features relating to the geometry, intensity, and gradients of the images were calculated for each visible lesion and for many false detections. Using a conventionally trained linear classifier we were able to achieve an Az of 0.84 while the multiobjective approach to training a linear classifier yielded an Az of 0.87 in the task of distinguishing between true lesions and false detections. Using a multiobjective approach to train a rulebased classifier with 5 thresholding rules resulted in an Az of 0.88 in the task of distinguishing between true lesions and false detections.
 Kupinski, M., Anastasio, M., & Giger, M. (2000). Multiobjective genetic optimization of diagnostic classifiers used in the computerized detection of mass lesions in mammography [397901]. PROCEEDINGSSPIE THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING, 4045.
 Anastasio, M. A., Kupinski, M. A., Nishikawa, R. M., & Giger, M. L. (1999). Multiobjective approach to optimizing compterized detection schemes. IEEE Nuclear Science Symposium and Medical Imaging Conference, 3, 18791883.More infoAbstract: This work addresses a multiobjective approach optimizing computeraided diagnosis (CAD) schemes. The multiobjective optimization problem admits a set of solutions, known as the Paretooptimal set. The performances of the Paretooptimal solutions can be interpreted as operating points on an optimal ROC or FROC curve, greater than or equal to the points on any possible ROC or FROC curve for a given dataset and given CAD classifier.
 Kupinski, M. A. (1999). Multiobjective genetic optimization of diagnostic classifiers with implications for generating receiver operating characteristic curves. IEEE Transactions on Medical Imaging, 18(8), 675685.More infoPMID: 10534050;Abstract: It is well understood that binary classifiers have two implicit objective functions (sensitivity and specificity) describing their performance. Traditional methods of classifier training attempt to combine these two objective functions (or two analogous class performance measures) into one so that conventional scalar optimization techniques can be utilized. This involves incorporating a priori information into the aggregation method so that the resulting performance of the classifier is satisfactory for the task at hand. We have investigated the use of a niched Pareto multiobjective genetic algorithm (GA) for classifier optimization. With niched Pareto GA's, an objective vector is optimized instead of a scalar function, eliminating the need to aggregate classification objective functions. The niched Pareto GA returns a set of optimal solutions that are equivalent in the absence of any information regarding the preferences of the objectives. The a priori knowledge that was used for aggregating the objective functions in conventional classifier training can instead be applied postoptimization to select from one of the series of solutions returned from the multiobjective genetic optimization. We have applied this technique to train a linear classifier and an artificial neural network (ANN), using simulated datasets. The performances of the solutions returned from the multiobjective genetic optimization represent a series of optimal (sensitivity, specificity) pairs, which can be thought of as operating points on a receiver operating characteristic (ROC) curve. All possible ROC curves for a given dataset and classifier are less than or equal to the ROC curve generated by the niched Pareto genetic optimization. Diagnostic classifiers, genetic algorithms, multiobjective optimization, ROC analysis. © 1999 IEEE.
 Kupinski, M. A., & Anastasio, M. A. (1999). Multiobjective genetic optimization of diagnostic classifiers with implications for generating receiver operating characteristic curves. Medical Imaging, IEEE Transactions on, 18(8), 675685.
 Kupinski, M. A., & Giger, M. L. (1999). Feature selection with limited datasets. Medical Physics, 26(10), 21762182.More infoPMID: 10535635;Abstract: Computeraided diagnosis has the potential of increasing diagnostic accuracy by providing a second reading to radiologists. In many computerized schemes, numerous features can be extracted to describe suspect image regions. A subset of these features is then employed in a data classifier to determine whether the suspect region is abnormal or normal. Different subsets of features will, in general, result in different classification performances. A feature selection method is often used to determine an 'optimal' subset of features to use with a particular classifier. A classifier performance measure (such as the area under the receiver operating characteristic curve) must be incorporated into this feature selection process. With limited datasets, however, there is a distribution in the classifier performance measure for a given classifier and subset of features. In this paper, we investigate the variation in the selected subset of 'optimal' features as compared with the true optimal subset of features caused by this distribution of classifier performance. We consider examples in which the probability that the optimal subset of features is selected can be analytically computed. We show the dependence of this probability on the dataset sample size, the total number of features from which to select, the number of features selected, and the performance of the true optimal subset. Once a subset of features has been selected, the parameters of the data classifier must be determined. We show that, with limited datasets and/or a large number of features from which to choose, bias is introduced if the classifier parameters are determined using the same data that were employed to select the 'optimal' subset of features.
 Kupinski, M. A., & Giger, M. L. (1999). Feature selection with limited datasets. Medical physics, 26(10), 21762182.
 Anastasio, M. A., & Kupinski, M. A. (1998). Optimization and FROC analysis of rulebased detection schemes using a multiobjective approach. IEEE Transactions on Medical Imaging, 17(6), 10891093.More infoPMID: 10048867;Abstract: Computerized detection schemes have the potential of increasing diagnostic accuracy in medical imaging by alerting radiologists to lesions that they initially overlooked. These schemes typically employ multiple parameters such as threshold values or filter weights to arrive at a detection decision. In order for the system to have high performance, the values of these parameters need to be set optimally. Conventional optimization techniques are designed to optimize a scalar objective function. The task of optimizing the performance of a computerized detection scheme, however, is clearly a multiobjective problem: we wish to simultaneously improve the sensitivity and falsepositive rate of the system. In this work we investigate a multiobjective approach to optimizing computerized rulebased detection schemes. In a multiobjective optimization, multiple objectives are simultaneously optimized, with the objective now being a vectorvalued function. The multiobjective optimization problem admits a set of solutions, known as the Paretooptimal set, which are equivalent in the absence of any information regarding the preferences of the objectives. The performances of the Paretooptimal solutions can be interpreted as operating points on an optimal freeresponse receiver operating characteristic (FROC) curve, greater than or equal to the points on any possible FROC curve for a given dataset and detection scheme. It is demonstrated that generating FROC curves in this manner eliminates several known problems with conventional FROC curve generation techniques for rulebased detection schemes. We employ the multiobjective approach to optimize a rulebased scheme for clustered microcalcification detection that has been developed in our laboratory. © 1999 IEEE.
 Anastasio, M. A., Kupinski, M. A., & Nishikawa, R. M. (1998). Optimization and FROC analysis of rulebased detection schemes using a multiobjective approach. Medical Imaging, IEEE Transactions on, 17(6), 10891093.
 Anastasio, M. A., Kupinski, M. A., & Pan, X. (1998). New classes of reconstruction methods in reflection mode diffraction tomography. Proceedings of the IEEE Ultrasonics Symposium, 1, 839842.More infoAbstract: Reflection mode diffraction tomography (DT) is an inversion scheme used to reconstruct the spatially variant refractive index distribution of a scattering object. We propose a linear strategy that makes use of the statistically complementary information inherent in the reflected scattered data to achieve a biasfree reduction of the image variance in two dimensional (2D) reflection mode DT. We derive infinite classes of estimation methods that can estimate the 2D Radon transform of the (bandpass filtered) scattering object function from the reflected scattered data. When the insonifying source is broadband we demonstrate that incorporation of the statistically complementary information generated by each frequency in the incident spectrum can further reduce the variance of the images reconstructed using different estimation methods.
 Anastasio, M. A., Kupinski, M. A., & Pan, X. (1998). Noise propagation in diffraction tomography: Comparison of conventional algorithms with a new reconstruction algorithm. Nuclear Science, IEEE Transactions on, 45(4), 22162223.
 Anastasio, M. A., Kupinski, M. A., & Pan, X. (1998). Noise propagation in diffraction tomography: comparison of conventional algorithms with a new reconstruction algorithm. IEEE Transactions on Nuclear Science, 45(4 PART 2), 22162223.More infoAbstract: In ultrasonic diffraction tomography, ultrasonic waves are used to probe the object of interest at various angles. The incident waves scatter when encountering inhomogeneities, unlike conventional Xray CT. Theoretically, when the scattering inhomogeneities are considered weak, the scattering object can be reconstructed by algorithms developed from a generalized central slice theorem. We develop a hybrid algorithm for reconstruction of a scattering object by transforming the scattered data into a conventional Xraylike sinogram thus allowing standard Xray reconstruction algorithms, such as filtered backprojection, to be applied. We investigate the statistical properties of the filtered backpropagation, direct Fourier, and newly proposed hybrid reconstruction algorithms by performing analytical as well as numerical studies. © 1998 IEEE.
 Kupinski, M. A., & Giger, M. L. (1998). Automated seeded lesion segmentation on digital mammograms. IEEE Transactions on Medical Imaging, 17(4), 510517.More infoPMID: 9845307;Abstract: Segmenting lesions is a vital step in many computerized massdetection schemes for digital (or digitized) mammograms. We have developed two novel lesion segmentation techniquesone based on a single feature called the radial gradient index (RGI) and one based on simple probabilistic models to segment mass lesions, or other similar nodular structures, from surrounding background. In both methods a series of image partitions is created using graylevel information as well as prior knowledge of the shape of typical mass lesions. With the former method the partition that maximizes the RGI is selected. In the latter method, probability distributions for graylevels inside and outside the partitions are estimated, and subsequently used to determine the probability that the image occurred for each given partition. The partition that maximizes this probability is selected as the final lesion partition (contour). We tested these methods against a conventional region growing algorithm using a database of biopsyproven, malignant lesions and found that the new lesion segmentation algorithms more closely match radiologists' outlines of these lesions. At an overlap threshold of 0.30, gray level region growing correctly delineates 62% of the lesions in our database while the RGI and probabilistic segmentation algorithms correctly segment 92% and 96% of the lesions, respectively. © 1998 IEEE.
 Kupinski, M. A., & Giger, M. L. (1998). Automated seeded lesion segmentation on digital mammograms. Medical Imaging, IEEE Transactions on, 17(4), 510517.
 Anastasio, M., Kupinski, M., & Pan, X. (1997). Noise properties of reconstructed images in ultrasonic diffraction tomography. IEEE Nuclear Science Symposium & Medical Imaging Conference, 2, 15611565.More infoAbstract: In ultrasonic diffraction tomography, ultrasonic waves are used to probe the object of interest at various angles. The incident waves scatter when encountering inhomogeneities, and thus do not travel in straight lines through the imaged object. When the scattering inhomogeneities are considered weak, the scattering object can be reconstructed by algorithms developed from a generalized central slice theorem. In this work, we develop a hybrid algorithm for reconstruction of a scattering object by transforming the measured scattered data into a conventional Xraylike sinogram thus allowing standard Xray reconstruction algorithms, such as filtered backprojection, to be applied. We systematically investigate and compare the statistical properties of three different algorithms: a direct Fourier inversion algorithm, the filtered backpropagation algorithm (which is analogous to the conventional filtered backprojection algorithm), and the newly developed hybrid algorithm. We derive analytical expressions for the variance of the noise in the reconstructed images and investigate the noise properties of the algorithms by performing extensive numerical simulations.
 Kupinski, M. A., & Giger, M. L. (1997). Feature selection and classifiers for the computerized detection of mass lesions in digital mammography. IEEE International Conference on Neural Networks  Conference Proceedings, 4, 24602463.More infoAbstract: We have investigated various methods of feature selection for two different data classifiers used in the computerized detection of mass lesions in digital mammograms. Numerous features were extracted from abnormal and normal breast regions from a database consisting of 210 individual mammograms. A stepwise method, a genetic algorithm and individual feature analysis were employed to select a subset of features to be used with linear discriminants. Similar techniques were also employed for an artificial neural network classifier. In both tests the genetic algorithm was able to either outperform or equal the performance of other methods.
 Kupinski, M. A., & Giger, M. L. (1997). Investigation of regularized neural networks for the computerized detection of mass lesions in digital mammograms. Annual International Conference of the IEEE Engineering in Medicine and Biology  Proceedings, 3, 13361339.More infoAbstract: Computerized schemes are currently being developed at the University of Chicago to detect mass lesions in digital mammograms. Artificial neural networks play an important role in the detection of masses. Currently, features are extracted from potential lesion areas and sent through a neural network to decide whether the area is to be called a true lesion or a false detection. One of the most difficult aspects of dealing with artificial neural networks is to train them without overtraining; in other words, to take both the bias and variance into account when training. Typically, an early stopping technique is employed; that is, the neural network is tested on an independent data set and training is stopped when the performance on this independent data set is maximized. In this paper the effectiveness of regularization is evaluated as a technique to minimize overtraining. Regularization adds an extra term to the costfunction used in neural network training that penalizes overcomplex results. The results of simulation studies will be presented along with results obtained using data of actual lesions and false positives from our computerized mass detection scheme.
 Kupinski, M., Giger, M., Lu, P., & Huo, Z. (1995). Computerized detection of mammographic lesions: performance of artificial neural network with enhanced feature extraction [243495]. PROCEEDINGSSPIE THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING, 598598.
Proceedings Publications
 Cronin, K. P., Kupinski, M. A., Barber, H. B., & Furenlid, L. R. (2022). Simulations and analysis of fluorescence effects in semiconductor xray and gammaray detectors. In Medical Imaging 2022: Physics of Medical Imaging, 12031.
 Doty, K. J., Kupinski, M. A., Richards, R. G., RuizGonzalez, M., King, M. A., Kuo, P. H., & Furenlid, L. R. (2022). Fisher information comparison between a monolithic and a fiberoptic light guide in a modular gamma camera. In Medical Imaging 2022: Physics of Medical Imaging, 12031.
 Kupinski, M. A., Clarkson, E. W., Cronin, K. P., Woolfenden, J. M., Humm, J. L., & Furenlid, L. R. (2022). Observer performance in multitechnology imaging. In Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment.
 RuizGonzalez, M., Richards, R. G., Doty, K. J., Kuo, P. H., Kupinski, M. A., Furenlid, L. R., & King, M. A. (2022). A readout strategy for highresolution largearea SiPMbased modular gammaray cameras. In Medical Imaging 2022: Physics of Medical Imaging, 12031.
 Barrett, H. H., Alberts, D. S., Woolfenden, J. M., Liu, Z., Clarkson, E. W., Kupinski, M. A., Furenlid, L. R., & Hoppin, J. (2015, august). Quantifying and Reducing Uncertainties in Cancer Therapy. In Proceedings of SPIE, 9412, 9412N4.
 Ghanbari, N., Kupinski, M. A., & Furenlid, L. R. (2015, August). Optimization of an adaptive SPECT system with the scanning linear estimate. In SPIE, 9594, 95940A.
 Ghanbari, N., Kupinski, M. A., & Furenlid, L. R. (2015, August). Optimization of an adaptive SPECT system with the scanning linear estimator. In SPIE, 9594, 95940A.
 Huang, J., Yao, J., Cirucci, N., Ivanov, T., & Rolland, J. P. (2015). Thickness estimation with optical coherence tomography and statistical decision theory. In SPIE Optifab.
 Huang, J., Yuan, Q., Tankam, P., Clarkson, E., Kupinski, M., Hindman, H. B., Aquavella, J. V., & Rolland, J. P. (2015). Application of maximumlikelihood estimation in optical coherence tomography for nanometerclass thickness estimation. In SPIE BiOS.
 Lin, A. L., Johnson, L. C., Shokouhi, S., Peterson, T. E., & Kupinski, M. A. (2015). Using the Wiener estimator to determine optimal imaging parameters in a syntheticcollimator SPECT system used for small animal imaging. In SPIE Medical Imaging.
 Tseng, H., Fan, J., & Kupinski, M. A. (2015). Combination of detection and estimation tasks using channelized scanning linear observer for CT imaging systems. In SPIE Medical Imaging.
 Wang, K., Lou, Y., Kupinski, M. A., & Anastasio, M. A. (2015). Sparsitydriven ideal observer for computed medical imaging systems. In SPIE Medical Imaging.
 Chaix, C., Kovalsky, S., Kupinski, M. A., Barrett, H. H., & Furenlid, L. R. (2014). Fabrication of the pinhole aperture for AdaptiSPECT. In SPIE Optical Engineering+ Applications, 921408921408.
 Chaix, C., Kovalsky, S., Kupinski, M. A., Barrett, H. H., & Furenlid, L. R. (2014, Fall). Design and fabrication of a preclinical adaptive SPECT imaging system: AdaptiSPECT. In GPSC Student Showcase.
 Huang, J., Clarkson, E., Kupinski, M., & Rolland, J. P. (2014). Simultaneous measurement of lipid and aqueous layers of tear film using optical coherence tomography and statistical decision theory. In SPIE BiOS, 89360A89360A.
 Tseng, H., Tseng, H., Fan, J., Fan, J., Kupinski, M. A., Kupinski, M. A., Sainath, P., & Sainath, P. (2014). Design of a practical modelobserverbased image quality assessment method for CT imaging systems. In SPIE Medical Imaging, 90370O90370O.
 Welge, W. A., DeMarco, A. T., Watson, J. M., Rice, P. S., Barton, J. K., & Kupinski, M. A. (2014). Objective assessment of multimodality optical coherence tomography and secondharmonic generation image quality of ex vivo mouse ovaries using human observers. In SPIE BiOS, 893609893609.
 Caucci, L., Jha, A. K., Furenlid, L. R., Clarkson, E. W., Kupinski, M. A., & Barrett, H. H. (2013). Image science with photonprocessing detectors. In Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2013 IEEE, 17.
 Dumas, C., Bernstein, A., Espinoza, A., Morgan, D., Lewis, K., Nipper, M., Barrett, H. H., Kupinski, M. A., & Furenlid, L. R. (2013). SmartCAM: an adaptive clinical SPECT camera. In SPIE Optical Engineering+ Applications, 885307885307.
 Fan, J., Tseng, H., Kupinski, M., Cao, G., Sainath, P., & Hsieh, J. (2013). Study of the radiation dose reduction capability of a CT reconstruction algorithm: LCD performance assessment using mathematical model observers. In SPIE Medical Imaging, 86731Q86731Q.
 Huang, J., Clarkson, E., Kupinski, M., & Rolland, J. P. (2013). Thickness Estimation with Optical Coherence Tomography and Statistical Decision Theory. In CIOMPOSA Summer Session on Optical Engineering, Design and Manufacturing, Tu9.
 Jha, A. K., Clarkson, E., Kupinski, M. A., & Barrett, H. H. (2013). Joint reconstruction of activity and attenuation map using LM SPECT emission data. In SPIE Medical Imaging, 86681W86681W.
 Huang, J., Lee, K., Clarkson, E., Kupinski, M., & Rolland, J. P. (2012). Taskbased Assessment and Optimization of Spectral Domain Optical Coherence Tomography for Tear Film Imaging. In Frontiers in Optics, FTu3A39.
 Jha, A., Kupinski, M., & Van Dam, H. (2011). Monte Carlo simulation of Silicon Photomultiplier output in response to scintillation induced light. In Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2011 IEEE, 16931696.
 Barrett, H. H., Wilson, D. W., Kupinski, M. A., Aguwa, K., Ewell, L., Hunter, R., & M\"uller, S. (2010). Therapy operating characteristic (TOC) curves and their application to the evaluation of segmentation algorithms. In SPIE Medical Imaging, 76270Z76270Z.
 Jha, A. K., Kupinski, M. A., Kang, D., & Clarkson, E. (2010). Solutions to the radiative transport equation for nonuniform media. In Biomedical Optics, BSuD55.
 Jha, A. K., Kupinski, M. A., Rodr\'\iguez, J. J., Stephen, R. M., & Stopeck, A. T. (2010). ADC estimation in multiscan DWMRI. In Digital Image Processing and Analysis, DTuB3.
 Jha, A. K., Kupinski, M. A., Rodr\'\iguez, J. J., Stephen, R. M., & Stopeck, A. T. (2010). Evaluating segmentation algorithms for diffusionweighted MR images: a taskbased approach. In SPIE Medical Imaging, 76270L76270L.
 Jha, A. K., Kupinski, M. A., Rodriguez, J., Stephen, R. M., & Stopeck, A. T. (2010). ADC estimation of lesions in diffusionweighted MR images: A maximumlikelihood approach. In Image Analysis \& Interpretation (SSIAI), 2010 IEEE Southwest Symposium on, 209212.
 Young, S., Kupinski, M. A., & Jha, A. K. (2010). Estimating signal detectability in a model diffuse optical imaging system. In Biomedical Optics, BSuD26.
 Palit, R., Kupinski, M. A., Barrett, H. H., Clarkson, E. W., Aarsvold, J. N., Volokh, L., & Grobshtein, Y. (2009). Singular value decomposition of pinhole SPECT systems. In SPIE Medical Imaging, 72631U72631U.
 Caucci, L., Kupinski, M. A., Freed, M., Furenlid, L. R., Wilson, D. W., & Barrett, H. H. (2008). Adaptive SPECT for tumor necrosis detection. In Nuclear Science Symposium Conference Record, 2008. NSS'08. IEEE, 55485551.
 Furenlid, L., Moore, J., Freed, M., Kupinski, M. A., Clarkson, E., Liu, Z., Wilson, D., Woolfenden, J., & Barrett, H. H. (2008). Adaptive smallanimal SPECT/CT. In Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on, 14071410.
 Br\`eme, A., Kupinski, M. A., Clarkson, E., & Barrett, H. H. (2007). Adaptive Hotelling discriminant functions. In Medical Imaging, 65150T65150T.
 Freed, M., Kupinski, M. A., Furenlid, L. R., & Barrett, H. H. (2007). A prototype instrument for adaptive SPECT imaging. In Medical Imaging, 65100V65100V.
 Hesterman, J. Y., Kupinski, M. A., Clarkson, E., Wilson, D. W., & Barrett, H. H. (2007). Evaluation of hardware in a smallanimal SPECT system using reconstructed images. In Medical Imaging, 65151G65151G.
 Kupinski, M. A., Clarkson, E., & Hesterman, J. Y. (2007). Bias in Hotelling observer performance computed from finite data. In Medical Imaging, 65150S65150S.
 Park, S., Clarkson, E., Barrett, H. H., Kupinski, M. A., & Myers, K. J. (2006). Performance of a channelizedideal observer using LaguerreGauss channels for detecting a Gaussian signal at a known location in different lumpy backgrounds. In Medical Imaging, 61460P61460P.
 Barrett, H. H., Clarkson, E., Furenlid, L. R., & Kupinski, M. (2005). Taskbased Assessment and Optimization of Gammaray Imaging Systems. In Frontiers in Optics, FThM1.
 Barrett, H. H., Kupinski, M. A., & Clarkson, E. (2005). Probabilistic foundations of the MRMC method. In Medical imaging, 2131.
 Gross, K. A., & Kupinski, M. A. (2005). SPECT Image Quality Assessment and System Parameter Optimization for Detection Tasks. In Frontiers in Optics, FThM2.
 Gross, K. A., Kupinski, M. A., & Hesterman, J. Y. (2005). A fast model of a multiplepinhole SPECT imaging system. In Medical Imaging, 118127.
 Hesterman, J. Y., Kupinski, M. A., Furenlid, L. R., & Wilson, D. W. (2005). Experimental taskbased optimization of a fourcamera variablepinhole smallanimal SPECT system. In Medical Imaging, 300309.
 Kupinski, M. A., & Clarkson, E. (2005). Extending the channelized Hotelling observer to account for signal uncertainty and estimation tasks. In Medical Imaging, 183190.
 Park, S., Clarkson, E., Kupinski, M. A., & Barrett, H. H. (2005). Efficiency of human and model observers for signaldetection tasks in nonGaussian distributed lumpy backgrounds. In Medical Imaging, 138149.
 Kupinski, M. A., & Clarkson, E. (2004). Imagequality assessment in optical tomography. In Biomedical Imaging: Nano to Macro, 2004. IEEE International Symposium on, 14711474.
 Park, S., Kupinski, M. A., Clarkson, E., & Barrett, H. H. (2004). Efficient channels for the ideal observer. In Medical Imaging 2004, 1221.
 Clarkson, E., Kupinski, M. A., & Hoppin, J. W. (2003). Assessing the accuracy of estimates of the likelihood ratio. In Medical Imaging 2003, 135143.
 Gross, K., Kupinski, M. A., Peterson, T. E., & Clarkson, E. (2003). Optimizing a multiplepinhole SPECT system using the ideal observer. In Medical Imaging 2003, 314322.
 Hoppin, J. W., Kupinski, M. A., Wilson, D. W., Peterson, T. E., Gershman, B., Kastis, G., Clarkson, E., Furenlid, L., & Barrett, H. H. (2003). Evaluating estimation techniques in medical imaging without a gold standard: experimental validation. In Medical Imaging 2003, 230237.
 Kupinski, M. A., Clarkson, E., Gross, K., & Hoppin, J. W. (2003). Optimizing imaging hardware for estimation tasks. In Medical Imaging 2003, 309313.
 Drukker, K., Giger, M., Horsch, K., Kupinski, M., Vyborny, C., & Mendelson, E. (2002). Automatic lesion detection on breast ultrasound. In RADIOLOGY, 222, 588588.
 Kupinski, M. A., Clakrson, E., & Barrett, H. H. (2002). Matching statistical object models to real images. In Medical Imaging 2002, 3742.
 Edwards, D. C., Papaioannou, J., Jiang, Y., Kupinski, M. A., & Nishikawa, R. M. (2001). Eliminating falsepositive microcalcification clusters in a mammography CAD scheme using a Bayesian neural network. In Medical Imaging 2001, 19541960.
 Edwards, D. C., Kupinski, M. A., Nishikawa, R. M., & Metz, C. E. (2000). Estimation of linear observer templates in the presence of multipeaked Gaussian noise through 2AFC experiments. In Medical Imaging 2000, 8696.
 Esthappan, J., Kupinski, M., Lan, L., & Hoffmann, K. (2000). A method for the determination of the 3D orientations and positions of catheters from singleplane Xray images. In Engineering in Medicine and Biology Society, 2000. Proceedings of the 22nd Annual International Conference of the IEEE, 3, 20292032.
 Kupinski, M. A., Anastasio, M. A., & Giger, M. L. (2000). Multiobjective genetic optimization of diagnostic classifiers used in the computerized detection of mass lesions in mammography. In Medical Imaging 2000, 4045.
 Kupinski, M., Giger, M., & Baehr, A. (1999). Computerized detection of mass lesions in digital mammography using radial gradient index filtering. In Radiology, 213, 229229.
 Nishikawa, R., Giger, M., Yarusso, L., Kupinski, M., Baehr, A., & Venta, L. (1999). Computeraided diagnosis (CAD) of images obtained on fullfield digital mammography. In Radiology, 213, 229229.
 Anastasio, M. A., Kupinski, M. A., Nishikawa, R. M., & Giger, M. L. (1998). A multiobjective approach to optimizing computerized detection schemes. In Nuclear Science Symposium, 1998. Conference Record. 1998 IEEE, 3, 18791883.
 Anastasio, M. A., Kupinski, M., & Pan, X. (1998). New classes of reconstruction methods in reflection mode diffraction tomography. In Ultrasonics Symposium, 1998. Proceedings., 1998 IEEE, 1, 839842.
 Kupinski, M., & Giger, M. (1998). Computeraided diagnosis: Feature selection with limited datasets. In RADIOLOGY, 209, 163163.
 Anastasio, M., Kupinski, M., & Pan, X. (1997). Noise properties of reconstructed images in ultrasonic diffraction tomography. In Nuclear Science Symposium, 1997. IEEE, 2, 15611565.
 Kupinski, M. A., & Giger, M. L. (1997). Feature selection and classifiers for the computerized detection of mass lesions in digital mammography. In Neural Networks, 1997., International Conference on, 4, 24602463.
 Kupinski, M., & Giger, M. (1997). Investigation of regularized neural networks for the computerized detection of mass lesions in digital mammograms. In Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the IEEE, 3, 13361339.
 Kupinski, M. A., Giger, M. L., Lu, P., & Huo, Z. (1995). Computerized detection of mammographic lesions: Performance of artificial neural network with enhanced feature extraction. In Medical Imaging 1995, 598605.
Others
 Brubaker, E., MacGahan, C., Kupinski, M., Hilton, N. R., & Johnson, W. C. (2015). Information Barriers for Imaging..
 Giger, M. L., & Kupinski, M. A. (2000). Method and system for the segmentation and classification of lesions.
 Kupinski, M. A. (2000). Computerized pattern classification in medical imaging.
 Kupinski, M. A. (2000). Investigation of Genetic Algorithms for ComputerAided Diagnosis.