Eric W Clarkson
- Professor, Medical Imaging
- Professor, Optical Sciences
- Professor, Applied Mathematics - GIDP
- M.S. Optical Sciences
- University of Arizona, Tucson, Arizona, United States
- Ph.D. Mathematics
- Arizona State University, Tempe, Arizona, United States
- M.S. Physics
- Arizona State University, Tempe, Arizona, United States
- B.A. Math, Physics, Philosophy
- Rice University, Houston, Texas, United States
- University of Arizona, Tucson, Arizona (2006 - Ongoing)
- University of Arizona (1998 - 2006)
Much of my current research effort is focused on developing methods to implement the task-based assessment of image quality for various medical imaging systems. I have participated in published work in this area for SPECT, MRI and OCT imaging systems. I am also currently involved with image quality projects for diffuse optical tomography (DOT) systems and X-Ray CT systems. In all of these areas our research team has developed mathematical observers, object and imaging system simulation methods, and task-based measures of observer performance that can be computed either directly from data or from reconstructed images. The tasks we have studied include detection of abnormalities, estimation of clinically relevant parameters, and tasks that combine classification and estimation. We have also developed some general theory on the computation of ideal-observer performance including Markov-chain Monte Carlo techniques, the estimation ROC curve, and surrogate figures of merit based on Fisher information. Any computation of a task-based figure of merit involves some error, and we have published work on methods for quantifying this error and predicting the degree to which it can be reduced by increasing the number of cases and/or observers. In summary, we have expertise in simulating medical imaging systems and quantifying their performance using medically relevant task performance. One important aspect in many of these imaging modalities is the kinetics of tracer distributions within the body of an animal or human subject. For dynamic imaging studies the time evolution of the tracer distribution must be accurately modeled in order to implement task-based assessment. To this end we have recently published work on a general theory for the combination of linear compartmental models with dynamic imaging. The result is what we call global compartmental models to distinguish them from localized compartmental models which are often used in imaging studies. This in turn has led to some work on estimating kinetic parameters from imaging data in both local and global compartmental models. The question of which kinetic parameters are estimable or identifiable from a given data set is often overlooked in imaging studies. In addition, even for identifiable parameters, the methods used to estimate them are often suboptimal from a statistical point of view. I believe that a mathematically and statistically rigorous approach to these problems can be of great value for dynamic imaging studies. We are also currently working on applying task-based methods to multimodality and adaptive imaging systems. A multimodality imaging system becomes adaptive when the information from one system is used to modify the the second system before data is taken with it. This modification may involve a physical change in the system or a change in the imaging protocol used for the second image acquisition. For example, the first system my be used to locate regions where abnormalities my be located, and then the second system is modified in such a way as to focus on the regions in the imaging. The combined system may then be evaluated based on task performance, and the adaptation rules modified in order to increase this performance. In this way we can optimize a multimodality adaptive system for a given imaging task.
Cnrtst Agnt Imaging+KintBME 522 (Spring 2021)
Cnrtst Agnt Imaging+KintOPTI 522 (Spring 2021)
Cnrtst Agnt Imaging+KintPHCL 522 (Spring 2021)
Prin Of Image ScienceOPTI 637 (Spring 2021)
Adv Math Methods For OpticsOPTI 604 (Fall 2020)
DissertationOPTI 920 (Fall 2020)
Independent StudyOPTI 599 (Fall 2020)
ResearchMATH 900 (Fall 2020)
Cnrtst Agnt Imaging+KintBME 522 (Spring 2020)
Cnrtst Agnt Imaging+KintCBIO 522 (Spring 2020)
Cnrtst Agnt Imaging+KintOPTI 522 (Spring 2020)
DissertationOPTI 920 (Spring 2020)
Independent StudyOPTI 599 (Spring 2020)
Prin Of Image ScienceOPTI 637 (Spring 2020)
ResearchMATH 900 (Spring 2020)
Adv Math Methods For OpticsOPTI 604 (Fall 2019)
DissertationOPTI 920 (Fall 2019)
Independent StudyMATH 599 (Fall 2019)
Cnrtst Agnt Imaging+KintBME 522 (Spring 2019)
DissertationOPTI 920 (Spring 2019)
Prin Of Image ScienceOPTI 637 (Spring 2019)
Master's ReportOPTI 909 (Winter 2018)
Adv Math Methods For OpticsOPTI 604 (Fall 2018)
DissertationOPTI 920 (Fall 2018)
Master's ReportOPTI 909 (Fall 2018)
ThesisOPTI 910 (Fall 2018)
Cnrtst Agnt Imaging+KintBME 524 (Spring 2018)
DissertationOPTI 920 (Spring 2018)
Prin Of Image ScienceOPTI 637 (Spring 2018)
Adv Math Methods For OpticsOPTI 604 (Fall 2017)
DissertationOPTI 920 (Fall 2017)
Cnrtst Agnt Imaging+KintBME 524 (Spring 2017)
Cnrtst Agnt Imaging+KintCBIO 524 (Spring 2017)
DissertationOPTI 920 (Spring 2017)
Prin Of Image ScienceOPTI 637 (Spring 2017)
Adv Math Methods For OpticsOPTI 604 (Fall 2016)
DissertationOPTI 920 (Fall 2016)
Cnrtst Agnt Imaging+KintBME 524 (Spring 2016)
Cnrtst Agnt Imaging+KintCBIO 524 (Spring 2016)
DissertationOPTI 920 (Spring 2016)
Prin Of Image ScienceOPTI 637 (Spring 2016)
- Clarkson, E. W. (2019). Probability of error for detecting a change in a parameter, total variation of the posterior distribution, and Bayesian Fisher information. Journal of the Optical Society of America A.
- Clarkson, E. W. (2019). Risk analysis, ideal observers and ROC curves for tasks that combine detection and estimation. Journal of Medical Imaging.
- Clarkson, E. W. (2019). The relation between Bayesian Fisher information and Shannon information for detecting a change in a parameter. Journal of the Optical Society of America A.
- Clarkson, E. W. (2018). Physiological random processes in precision cancer therapy. PLoS One.
- Clarkson, E. W., Ghanbari, N., Kupinski, M., & LI, X. (2017). Optimization of an adaptive SPECT system with the scanning linear estimator. IEEE Transactions on Radiation and Plasma Medical Sciences, 1, 435-443.
- Bora, V., Barrett, H. H., Fastje, D., Clarkson, E., Furenlid, L., Bousselham, A., Shah, K. S., & Glodo, J. (2016). Estimation of Fano factor in inorganic scintillators. Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment, 805, 72-86.More infoThe Fano factor of an integer-valued random variable is defined as the ratio of its variance to its mean. Correlation between the outputs of two photomultiplier tubes on opposite faces of a scintillation crystal was used to estimate the Fano factor of photoelectrons and scintillation photons. Correlations between the integrals of the detector outputs were used to estimate the photoelectron and photon Fano factor for YAP:Ce, SrI2:Eu and CsI:Na scintillator crystals. At 662 keV, SrI2:Eu was found to be sub-Poisson, while CsI:Na and YAP:Ce were found to be super-Poisson. An experiment setup inspired from the Hanbury Brown and Twiss experiment was used to measure the correlations as a function of time between the outputs of two photomultiplier tubes looking at the same scintillation event. A model of the scintillation and the detection processes was used to generate simulated detector outputs as a function of time for different values of Fano factor. The simulated outputs from the model for different Fano factors was compared to the experimentally measured detector outputs to estimate the Fano factor of the scintillation photons for YAP:Ce, LaBr3:Ce scintillator crystals. At 662 keV, LaBr3:Ce was found to be sub-Poisson, while YAP:Ce was found to be close to Poisson.
- Clarkson, E. W., & Cushing, J. (2016). Shannon Information for joint estimation/detection tasks in imaging. Journal of the Optical Society of America A, 292, 286.
- Clarkson, E., & Barrett, H. H. (2016). Characteristic functionals in imaging and image-quality assessment: tutorial. Journal of the Optical Society of America. A, Optics, image science, and vision, 33(8), 1464-75.More infoCharacteristic functionals are one of the main analytical tools used to quantify the statistical properties of random fields and generalized random fields. The viewpoint taken here is that a random field is the correct model for the ensemble of objects being imaged by a given imaging system. In modern digital imaging systems, random fields are not used to model the reconstructed images themselves since these are necessarily finite dimensional. After a brief introduction to the general theory of characteristic functionals, many examples relevant to imaging applications are presented. The propagation of characteristic functionals through both a binned and list-mode imaging system is also discussed. Methods for using characteristic functionals and image data to estimate population parameters and classify populations of objects are given. These methods are based on maximum likelihood and maximum a posteriori techniques in spaces generated by sampling the relevant characteristic functionals through the imaging operator. It is also shown how to calculate a Fisher information matrix in this space. These estimators and classifiers, and the Fisher information matrix, can then be used for image quality assessment of imaging systems.
- Clarkson, E., & Cushing, J. B. (2016). Shannon information and receiver operating characteristic analysis for multiclass classification in imaging. Journal of the Optical Society of America. A, Optics, image science, and vision, 33(5), 930-7.More infoWe show how Shannon information is mathematically related to receiver operating characteristic (ROC) analysis for multiclass classification problems in imaging. In particular, the minimum probability of error for the ideal observer, as a function of the prior probabilities for each class, determines the Shannon Information for the classification task, also considered as a function of the prior probabilities on the classes. In the process, we show how an ROC hypersurface that has been studied by other researchers is mathematically related to a Shannon information ROC (SIROC) hypersurface. In fact, the ROC hypersurface completely determines the SIROC hypersurface via a non-local integral transform on the ROC hypersurface. We also show that both hypersurfaces are convex and satisfy other geometrical relationships via the Legendre transform.
- Kupinski, M. K., & Clarkson, E. (2016). Optimal channels for channelized quadratic estimators. Journal of the Optical Society of America. A, Optics, image science, and vision, 33(6), 1214-25.More infoWe present a new method for computing optimized channels for estimation tasks that is feasible for high-dimensional image data. Maximum-likelihood (ML) parameter estimates are challenging to compute from high-dimensional likelihoods. The dimensionality reduction from M measurements to L channels is a critical advantage of channelized quadratic estimators (CQEs), since estimating likelihood moments from channelized data requires smaller sample sizes and inverting a smaller covariance matrix is easier. The channelized likelihood is then used to form ML estimates of the parameter(s). In this work we choose an imaging example in which the second-order statistics of the image data depend upon the parameter of interest: the correlation length. Correlation lengths are used to approximate background textures in many imaging applications, and in these cases an estimate of the correlation length is useful for pre-whitening. In a simulation study we compare the estimation performance, as measured by the root-mean-squared error (RMSE), of correlation length estimates from CQE and power spectral density (PSD) distribution fitting. To abide by the assumptions of the PSD method we simulate an ergodic, isotropic, stationary, and zero-mean random process. These assumptions are not part of the CQE formalism. The CQE method assumes a Gaussian channelized likelihood that can be a valid for non-Gaussian image data, since the channel outputs are formed from weighted sums of the image elements. We have shown that, for three or more channels, the RMSE of CQE estimates of correlation length is lower than conventional PSD estimates. We also show that computing CQE by using a standard nonlinear optimization method produces channels that yield RMSE within 2% of the analytic optimum. CQE estimates of anisotropic correlation length estimation are reported to demonstrate this technique on a two-parameter estimation problem.
- Bora, V., Barrett, H. H., Jha, A. K., & Clarkson, E. (2015). Impact of the Fano Factor on Position and Energy Estimation in Scintillation Detectors. IEEE transactions on nuclear science, 62(1), 42-56.More infoThe Fano factor for an integer-valued random variable is defined as the ratio of its variance to its mean. Light from various scintillation crystals have been reported to have Fano factors from sub-Poisson (Fano factor < 1) to super-Poisson (Fano factor > 1). For a given mean, a smaller Fano factor implies a smaller variance and thus less noise. We investigated if lower noise in the scintillation light will result in better spatial and energy resolutions. The impact of Fano factor on the estimation of position of interaction and energy deposited in simple gamma-camera geometries is estimated by two methods - calculating the Cramér-Rao bound and estimating the variance of a maximum likelihood estimator. The methods are consistent with each other and indicate that when estimating the position of interaction and energy deposited by a gamma-ray photon, the Fano factor of a scintillator does not affect the spatial resolution. A smaller Fano factor results in a better energy resolution.
- Bora, V., Barrett, H. H., Jha, A., & Clarkson, E. W. (2015). Impact of Fano factor on position and energy estimation in scintillation detectors. IEEE Transactions in Nuclear Science, 62, 42-56.
- Bora, V., Barrett, H. H., Jha, A., & Clarkson, E. W. (2015). Impact of the Fano Factor on Position and Energy Estimation in Scintillation Detectors. IEEE Trans Nucl Sci., 62(1), 42-56. doi:10.1109/TNS.2014.2379620
- Clarkson, E., & Cushing, J. B. (2015). Shannon information and ROC analysis in imaging. Journal of the Optical Society of America. A, Optics, image science, and vision, 32(7), 1288-301.More infoShannon information (SI) and the ideal-observer receiver operating characteristic (ROC) curve are two different methods for analyzing the performance of an imaging system for a binary classification task, such as the detection of a variable signal embedded within a random background. In this work we describe a new ROC curve, the Shannon information receiver operator curve (SIROC), that is derived from the SI expression for a binary classification task. We then show that the ideal-observer ROC curve and the SIROC have many properties in common, and are equivalent descriptions of the optimal performance of an observer on the task. This equivalence is described mathematically by an integral transform that maps the ideal-observer ROC curve onto the SIROC. This then leads to an integral transform relating the minimum probability of error, as a function of the odds against a signal, to the conditional entropy, as a function of the same variable. This last relation then gives us the complete mathematical equivalence between ideal-observer ROC analysis and SI analysis of the classification task for a given imaging system. We also find that there is a close relationship between the area under the ideal-observer ROC curve, which is often used as a figure of merit for imaging systems and the area under the SIROC. Finally, we show that the relationships between the two curves result in new inequalities relating SI to ROC quantities for the ideal observer.
- Huang, C., Galons, J., Graff, C. G., Clarkson, E. W., Bilgin, A., Kalb, B., Martin, D. R., & Altbach, M. I. (2015). Correcting partial volume effects in biexponential T2 estimation of small lesions. Magnetic resonance in medicine, 73(4), 1632-42.More infoT2 mapping provides a quantitative approach for focal liver lesion characterization. For small lesions, a biexponential model should be used to account for partial volume effects (PVE). However, conventional biexponential fitting suffers from large uncertainty of the fitted parameters when noise is present. The purpose of this work is to develop a more robust method to correct for PVE affecting small lesions.
- Jha, A. K., Barrett, H. H., Frey, E. C., Clarkson, E., Caucci, L., & Kupinski, M. A. (2015). Singular value decomposition for photon-processing nuclear imaging systems and applications for reconstruction and computing null functions. Physics in medicine and biology, 60(18), 7359-85.More infoRecent advances in technology are enabling a new class of nuclear imaging systems consisting of detectors that use real-time maximum-likelihood (ML) methods to estimate the interaction position, deposited energy, and other attributes of each photon-interaction event and store these attributes in a list format. This class of systems, which we refer to as photon-processing (PP) nuclear imaging systems, can be described by a fundamentally different mathematical imaging operator that allows processing of the continuous-valued photon attributes on a per-photon basis. Unlike conventional photon-counting (PC) systems that bin the data into images, PP systems do not have any binning-related information loss. Mathematically, while PC systems have an infinite-dimensional null space due to dimensionality considerations, PP systems do not necessarily suffer from this issue. Therefore, PP systems have the potential to provide improved performance in comparison to PC systems. To study these advantages, we propose a framework to perform the singular-value decomposition (SVD) of the PP imaging operator. We use this framework to perform the SVD of operators that describe a general two-dimensional (2D) planar linear shift-invariant (LSIV) PP system and a hypothetical continuously rotating 2D single-photon emission computed tomography (SPECT) PP system. We then discuss two applications of the SVD framework. The first application is to decompose the object being imaged by the PP imaging system into measurement and null components. We compare these components to the measurement and null components obtained with PC systems. In the process, we also present a procedure to compute the null functions for a PC system. The second application is designing analytical reconstruction algorithms for PP systems. The proposed analytical approach exploits the fact that PP systems acquire data in a continuous domain to estimate a continuous object function. The approach is parallelizable and implemented for graphics processing units (GPUs). Further, this approach leverages another important advantage of PP systems, namely the possibility to perform photon-by-photon real-time reconstruction. We demonstrate the application of the approach to perform reconstruction in a simulated 2D SPECT system. The results help to validate and demonstrate the utility of the proposed method and show that PP systems can help overcome the aliasing artifacts that are otherwise intrinsically present in PC systems.
- Jha, A., Barrett, H. H., Frey, E. C., Clarkson, E. W., Caucci, L., & Kupinski, M. A. (2015). Singular value decomposition for photon-processing nuclear imaging systems and applications for reconstruction and computing null functions. Physics in Medicine and Biology, 6(18), 7359-7385.
- Kupinski, M. K., & Clarkson, E. W. (2015). Method for optimizing channelized quadratic observers for binary classification of large-dimensional data sets. Journal of the Optical Society of America, 32(4), 549-565.
- Clarkson, E., & Clarkson, E. W. (2012). Asymptotic ideal observers and surrogate figures of merit for signal detection with list-mode data. Journal of the Optical Society of America. A, Optics, image science, and vision, 29(10).More infoThe asymptotic form for the likelihood ratio is derived for list-mode data generated by an imaging system viewing a possible signal in a randomly generated background. This calculation provides an approximation to the likelihood ratio that is valid in the limit of large number of list entries, i.e., a large number of photons. These results are then used to derive surrogate figures of merit, quantities that are correlated with ideal-observer performance on detection tasks, as measured by the area under the receiver operating characteristic curve, but are easier to compute. A key component of these derivations is the determination of asymptotic forms for the Fisher information for the signal amplitude in the limit of a large number of counts or a long exposure time. This quantity is useful in its own right as a figure of merit (FOM) for the task of estimating the signal amplitude. The use of the Fisher information in detection tasks is based on the fact that it provides an approximation for ideal-observer detectability when the signal is weak. For both the fixed-count and fixed-time cases, four surrogate figures of merit are derived. Two are based on maximum likelihood reconstructions; one uses the characteristic functional of the random backgrounds. The fourth surrogate FOM is identical in the two cases and involves an integral over attribute space for each of a randomly generated sequence of backgrounds.
- Clarkson, E., & Clarkson, E. W. (2007). Estimation receiver operating characteristic curve and ideal observers for combined detection/estimation tasks. Journal of the Optical Society of America. A, Optics, image science, and vision, 24(12).More infoThe localization receiver operating characteristic (LROC) curve is a standard method to quantify performance for the task of detecting and locating a signal. This curve is generalized to arbitrary detection/estimation tasks to give the estimation ROC (EROC) curve. For a two-alternative forced-choice study, where the observer must decide which of a pair of images has the signal and then estimate parameters pertaining to the signal, it is shown that the average value of the utility on those image pairs where the observer chooses the correct image is an estimate of the area under the EROC curve (AEROC). The ideal LROC observer is generalized to the ideal EROC observer, whose EROC curve lies above those of all other observers for the given detection/estimation task. When the utility function is nonnegative, the ideal EROC observer is shown to share many mathematical properties with the ideal observer for the pure detection task. When the utility function is concave, the ideal EROC observer makes use of the posterior mean estimator. Other estimators that arise as special cases include maximum a posteriori estimators and maximum-likelihood estimators.
- Clarkson, E., Shen, F., & Clarkson, E. W. (2006). Using Fisher information to approximate ideal-observer performance on detection tasks for lumpy-background images. Journal of the Optical Society of America. A, Optics, image science, and vision, 23(10).More infoWhen building an imaging system for detection tasks in medical imaging, we need to evaluate how well the system performs before we can optimize it. One way to do the evaluation is to calculate the performance of the Bayesian ideal observer. The ideal-observer performance is often computationally expensive, and it is very useful to have an approximation to it. We use a parameterized probability density function to represent the corresponding densities of data under the signal-absent and the signal-present hypotheses. We develop approximations to the ideal-observer detectability as a function of signal parameters involving the Fisher information matrix, which is normally used in parameter estimation problems. The accuracy of the approximation is illustrated in analytical examples and lumpy-background simulations. We are able to predict the slope of the detectability as a function of the signal parameter. This capability suggests that the Fisher information matrix itself evaluated at the null parameter value can be used as the figure of merit in imaging system evaluation. We are also able to provide a theoretical foundation for the connection between detection tasks and estimation tasks.
- Clarkson, E., & Clarkson, E. W. (2002). Bounds on the area under the receiver operating characteristic curve for the ideal observer. Journal of the Optical Society of America. A, Optics, image science, and vision, 19(10).More infoA new upper bound is derived on the area under the receiver operating characteristic curve for the ideal observer in a signal-detection task. This upper bound is determined by the values of the likelihood-generating function and its second derivative at the origin. This bound is compared with other bounds on ideal-observer performance that have been derived recently, and it is also shown how this bound leads to some asymptotic results for approximations to ideal-observer performance.
- Clarkson, E., Wilson, D. W., Barrett, H. H., & Clarkson, E. W. (2000). Reconstruction of two- and three-dimensional images from synthetic-collimator data. IEEE transactions on medical imaging, 19(5).More infoA novel SPECT collimation method, termed the synthetic collimator, is proposed. The synthetic collimator employs a multiple-pinhole aperture and a high-resolution detector. The problem of multiplexing, normally associated with multiple pinholes, is reduced by obtaining projections at a number of pinhole-detector distances. Projections with little multiplexing are collected at small pinhole-detector distances and high-resolution projections are collected at greater pinhole-detector distances. These projections are then reconstructed using the ML-EM algorithm. It is demonstrated through computer simulations that the synthetic collimator has superior resolution properties to a high-resolution parallel-beam (HRPB) collimator and a specially built ultra-high-resolution parallel-beam (UHRPB) collimator designed for our 0.38-mm pixel CdZnTe detectors. It is also shown that reconstructing images in three dimensions is superior to reconstructing them in two dimensions. The advantages of a high-resolution synthetic collimator over the parallel-hole collimators are apparently reduced in the presence of statistical noise. However, a high-sensitivity synthetic collimator was designed which again shows superior properties to the parallel-hole collimators. Finally, it is demonstrated that, for the cases studied, high-resolution detectors are necessary for the proper functionality of the synthetic collimator.
- Clarkson, E., Barrett, H. H., Abbey, C. K., & Clarkson, E. W. (1998). Objective assessment of image quality. III. ROC metrics, ideal observers, and likelihood-generating functions. Journal of the Optical Society of America. A, Optics, image science, and vision, 15(6).More infoWe continue the theme of previous papers [J. Opt. Soc. Am. A 7, 1266 (1990); 12, 834 (1995)] on objective (task-based) assessment of image quality. We concentrate on signal-detection tasks and figures of merit related to the ROC (receiver operating characteristic) curve. Many different expressions for the area under an ROC curve (AUC) are derived for an arbitrary discriminant function, with different assumptions on what information about the discriminant function is available. In particular, it is shown that AUC can be expressed by a principal-value integral that involves the characteristic functions of the discriminant. Then the discussion is specialized to the ideal observer, defined as one who uses the likelihood ratio (or some monotonic transformation of it, such as its logarithm) as the discriminant function. The properties of the ideal observer are examined from first principles. Several strong constraints on the moments of the likelihood ratio or the log likelihood are derived, and it is shown that the probability density functions for these test statistics are intimately related. In particular, some surprising results are presented for the case in which the log likelihood is normally distributed under one hypothesis. To unify these considerations, a new quantity called the likelihood-generating function is defined. It is shown that all moments of both the likelihood and the log likelihood under both hypotheses can be derived from this one function. Moreover, the AUC can be expressed, to an excellent approximation, in terms of the likelihood-generating function evaluated at the origin. This expression is the leading term in an asymptotic expansion of the AUC; it is exact whenever the likelihood-generating function behaves linearly near the origin. It is also shown that the likelihood-generating function at the origin sets a lower bound on the AUC in all cases.
- Clarkson, E. W. (2016, May). Figures of merit for optimizing imaging systems on joint estimation/detection tasks. In Proceedings SPIE, 9847.
- Clarkson, E. W., Bora, V., Fastje, D., Shaw, K. S., Shirwadkar, U., & Barrett, H. H. (2015, August). Negative temporal cross-covariance in SrI2:Eu. In Proceedings SPIE, 9787.
- Clarkson, E. W., Cushing, J. B., Mandava, S., & Bilgin, A. (2016, May). Estimation and detection information trade-off for x-ray system optimization. In Proceedings SPIE, 9847.
- Clarkson, E. W., Ghanbari, N., & Kupinski, M. A. (2015, August). Optimization of an adaptive SPECT system with the scanning linear estimator. In Proceedings SPIE, 9594.
- Clarkson, E. W., Huang, J., Yuan, Q., Tankam, P., Kupinski, M., Hindman, H. B., Aquavella, J. V., & Rolland, J. (2015, February). Application of maximum-likelihood estimation in optical coherence tomography for nanometer-class thickness estimation. In Proceedings SPIE, 9315.
- Clarkson, E. W., Kupinski, M. K., Ghaly, M., & Frey, E. (2016, August). Applying the J-optimal channelized quadratic observer to SPECT myocardial perfusion detection. In Proceedings SPIE, 9787.
- Clarkson, E. W., Zachary, W. D., Dereniak, E., & Furenlid, L. (2016, May). An automated method for registering liar data in restrictive tunnel-like environments. In Proceedings SPIE, 9832.
- Kupinski, M. K., Clarkson, E. W., Ghaly, M., & Frey, E. (2016, Spring). Applying the J-optimal channelized quadratic observer to SPECT myocardial perfusion detection. In SPIE Proceedings Medical Imaging.
- 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, 9412N-4.
- Clarkson, E. W. (2018, Fall). Signal Detection, Part 3 of Image quality and Statistical Analysis short Course. IEEE MIC.
- Clarkson, E. W. (2018, Fall). Statistical Image Reconstruction, Part 5 of Image Quality and Statistical Analysis Short Course. IEEE MIC.
- Clarkson, E. W. (2017, Fall). Use of characteristic functionals to analyze molecular images in targeted cancer therapy. IEEE MIC.
- Clarkson, E. W., Kupinski, M., & Furenlid, L. (2016, October). Image Quality and Statistical Analysis Short Course. IEEE NSS/MIC. Strasbourg, France: IEEE.
- Clarkson, E. W., Kupinski, M., & Furenlid, L. (2017, October). Image Quality and Statistical Anlysis. IEEE NSS/MIC. Atlanta, GA: IEEE.
- Huang, J., Clarkson, E. W., Pattanaik, S., & Ashok, A. (2017, August). Direct and Indirect Photon Pathway Imaging: An Information Theoretic Analysis. SPIE Optics and Photonics. San Diego, CA: SPIE.
- Ashok, A., Neifeld, M. A., Clarkson, E. W., & Gehm, M. E. (2015, October, 2015). Information-‐Theoretic System Analysis and Design Framework: Advanced X-‐ray Explosive Threat Detection. ALERT - ADSA Workshop 13 - Northeastern University. Boston, MA: Northeastern University.