Luca Caucci
 Assistant Professor, Research Scholar Track
 Assistant Research Professor, Optical Sciences
Contact
 (520) 6264162
 AHSC, Rm. 1343
 TUCSON, AZ 857245067
 caucci@email.arizona.edu
Degrees
 Ph.D. Optical Scieces
 University of Arizona, Tucson, Arizona, United States
 Task Performance with ListMode Data
 M.S. Optical Sciences
 University of Arizona, Tucson, Arizona, United States
 M.S. Electrical and Computer Engineering
 University of Arizona, Tucson, Arizona, United States
 Point Detection and Hotelling Discriminant: An Applicationin Adaptive Optics
Work Experience
 University of Arizona, Tucson, Arizona (2015  Ongoing)
Awards
 Senior Member
 National Academy of Inventors, Summer 2020
 Outstanding Graduate Student Award
 College of Optical Sciences, University of Arizona, Spring 2011
Interests
Research
Detection, estimation, listmode data, emission tomography, parallel computing
Courses
202021 Courses

Advanced Medical Imaging
OPTI 638 (Spring 2021)
201920 Courses

Intro to Image Science
OPTI 536 (Spring 2020)
201819 Courses

Advanced Medical Imaging
OPTI 638 (Spring 2019)
201718 Courses

Advanced Medical Imaging
BME 638 (Spring 2018) 
Advanced Medical Imaging
OPTI 638 (Spring 2018)
201516 Courses

Advanced Medical Imaging
BME 638 (Spring 2016) 
Advanced Medical Imaging
OPTI 638 (Spring 2016)
Scholarly Contributions
Journals/Publications
 Caucci, L., & Barrett, H. H. (2020). Stochastic models for objects and images in oncology and virology: Application to PI3KAktmTOR signaling and COVID19 disease. Journal of medical imaging (Bellingham, Wash.).
 Barrett, H. H., Furenlid, L. R., Han, H., Jha, A. K., Liu, Z., & Caucci, L. (2019). Towards ContinuoustoContinuous 3D Imaging in the Real World. Physics in Medicine and Biology.More infoImaging systems are often modeled as continuoustodiscrete mappings that map the object (i.e., a function of continuous variables such as space, time, energy, wavelength, etc.) to a finite set of measurements. When it comes to reconstruction, some discretized version of the object is almost always assumed, leading to a discretetodiscrete representation of the imaging system. In this paper, we discuss a method for single photon emission computed tomography~(SPECT) imaging that avoids discrete representations of the object or the imaging system, thus allowing reconstruction on arbitrarily fine set of points.
 Caucci, L., Ding, Y., & Barrett, H. H. (2017). Null functions in threedimensional imaging of alpha and beta particles. Scientific Reports.
 Caucci, L., Myers, K. J., & Barrett, H. H. (2016). Radiance and photon noise: imaging in geometrical optics, physical optics, quantum optics and radiology. Optical Engineering, 55(1), 013102. doi:10.1117/1.OE.55.1.013102.
 Barrett, H. H., Alberts, D. S., Woolfenden, J. M., Liu, Z., Caucci, L., & Hoppin, J. W. (2015). Quantifying and Reducing Uncertainties in Cancer Therapy. Proceedings of SPIEthe International Society for Optical Engineering, 9412.More infoThere are two basic sources of uncertainty in cancer chemotherapy: how much of the therapeutic agent reaches the cancer cells, and how effective it is in reducing or controlling the tumor when it gets there. There is also a concern about adverse effects of the therapy drug. Similarly in externalbeam radiation therapy or radionuclide therapy, there are two sources of uncertainty: delivery and efficacy of the radiation absorbed dose, and again there is a concern about radiation damage to normal tissues. The therapy operating characteristic (TOC) curve, developed in the context of radiation therapy, is a plot of the probability of tumor control vs. the probability of normaltissue complications as the overall radiation dose level is varied, e.g. by varying the beam current in externalbeam radiotherapy or the total injected activity in radionuclide therapy. The TOC can be applied to chemotherapy with the administered drug dosage as the variable. The area under a TOC curve (AUTOC) can be used as a figure of merit for therapeutic efficacy, analogous to the area under an ROC curve (AUROC), which is a figure of merit for diagnostic efficacy. In radiation therapy AUTOC can be computed for a single patient by using image data along with radiobiological models for tumor response and adverse side effects. In this paper we discuss the potential of using mathematical models of drug delivery and tumor response with imaging data to estimate AUTOC for chemotherapy, again for a single patient. This approach provides a basis for truly personalized therapy and for rigorously assessing and optimizing the therapy regimen for the particular patient. A key role is played by Emission Computed Tomography (PET or SPECT) of radiolabeled chemotherapy drugs.
 Jha, A. K., Barrett, H. H., Frey, E. C., Clarkson, E., 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, 60(18), 735985.More infoRecent advances in technology are enabling a new class of nuclear imaging systems consisting of detectors that use realtime maximumlikelihood (ML) methods to estimate the interaction position, deposited energy, and other attributes of each photoninteraction event and store these attributes in a list format. This class of systems, which we refer to as photonprocessing (PP) nuclear imaging systems, can be described by a fundamentally different mathematical imaging operator that allows processing of the continuousvalued photon attributes on a perphoton basis. Unlike conventional photoncounting (PC) systems that bin the data into images, PP systems do not have any binningrelated information loss. Mathematically, while PC systems have an infinitedimensional 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 singularvalue decomposition (SVD) of the PP imaging operator. We use this framework to perform the SVD of operators that describe a general twodimensional (2D) planar linear shiftinvariant (LSIV) PP system and a hypothetical continuously rotating 2D singlephoton 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 photonbyphoton realtime 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 photonprocessing nuclear imaging systems and applications for reconstruction and computing null functions. Physics in Medicine and Biology, 6(18), 73597385.
 Caucci, L., Myers, K. J., & Barrett, H. H. (2014). Radiance and photon noise: imaging in geometrical optics, physical optics, quantum optics and radiology. OPTICAL ENGINEERING, 55(1).
 Caucci, L., & Barrett, H. H. (2012). Objective assessment of image quality. V. Photoncounting detectors and listmode data. Journal of the Optical Society of America. A, Optics, image science, and vision, 29(6), 100316.More infoA theoretical framework for detection or discrimination tasks with listmode data is developed. The object and imaging system are rigorously modeled via three random mechanisms: randomness of the object being imaged, randomness in the attribute vectors, and, finally, randomness in the attribute vector estimates due to noise in the detector outputs. By considering the listmode data themselves, the theory developed in this paper yields a manageable expression for the likelihood of the listmode data given the object being imaged. This, in turn, leads to an expression for the optimal Bayesian discriminant. Figures of merit for detection tasks via the ideal and optimal linear observers are derived. A concrete example discusses detection performance of the optimal linear observer for the case of a known signal buried in a random lumpy background.
 Spaletta, G., & Caucci, L. (2012). Constrained iterations for blind deconvolution and convexity issues. JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, 197(1), 2943.
 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), 10771084.More infoA 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.
 Caucci, L., Barrett, H. H., & Rodriguez, J. J. (2009). Spatiotemporal Hotelling observer for signal detection from image sequences. Optics express, 17(13), 1094658.More infoDetection of signals in noisy images is necessary in many applications, including astronomy and medical imaging. The optimal linear observer for performing a detection task, called the Hotelling observer in the medical literature, can be regarded as a generalization of the familiar prewhitening matched filter. Performance on the detection task is limited by randomness in the image data, which stems from randomness in the object, randomness in the imaging system, and randomness in the detector outputs due to photon and readout noise, and the Hotelling observer accounts for all of these effects in an optimal way. If multiple temporal frames of images are acquired, the resulting data set is a spatiotemporal random process, and the Hotelling observer becomes a spatiotemporal linear operator. This paper discusses the theory of the spatiotemporal Hotelling observer and estimation of the required spatiotemporal covariance matrices. It also presents a parallel implementation of the observer on a cluster of Sony PLAYSTATION 3 gaming consoles. As an example, we consider the use of the spatiotemporal Hotelling observer for exoplanet detection.
 Caucci, L., Furenlid, L. R., & Barrett, H. H. (2009). Maximum Likelihood Event Estimation and Listmode Image Reconstruction on GPU Hardware. IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium, 2009, 4072.More infoThe scintillation detectors commonly used in SPECT and PET imaging and in Compton cameras require estimation of the position and energy of each gamma ray interaction. Ideally, this process would yield images with no spatial distortion and the best possible spatial resolution. In addition, especially for Compton cameras, the computation must yield the best possible estimate of the energy of each interacting gamma ray. These goals can be achieved by use of maximumlikelihood (ML) estimation of the event parameters, but in the past the search for an ML estimate has not been computationally feasible. Now, however, graphics processing units (GPUs) make it possible to produce optimal, realtime estimates of position and energy, even from scintillation cameras with a large number of photodetectors. In addition, the mathematical properties of ML estimates make them very attractive for use as list entries in listmode ML image reconstruction. This twostep ML processusing ML estimation once to get the list data and again to reconstruct the objectallows accurate modeling of the detector blur and, potentially, considerable improvement in reconstructed spatial resolution.
 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. Nuclear Science Symposium, 2008, 55485551.More infoIn 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.
 Caucci, L., Barrett, H. H., Devaney, N., & Rodríguez, J. J. (2007). Application of the Hotelling and ideal observers to detection and localization of exoplanets. Journal of the Optical Society of America. A, Optics, image science, and vision, 24(12), B1324.More infoThe ideal linear discriminant or Hotelling observer is widely used for detection tasks and imagequality assessment in medical imaging, but it has had little application in other imaging fields. We apply it to detection of planets outside of our solar system with longexposure images obtained from groundbased or spacebased telescopes. The statistical limitations in this problem include Poisson noise arising mainly from the host star, electronic noise in the image detector, randomness or uncertainty in the pointspread function (PSF) of the telescope, and possibly a random background. PSF randomness is reduced but not eliminated by the use of adaptive optics. We concentrate here on the effects of Poisson and electronic noise, but we also show how to extend the calculation to include a random PSF. For the case where the PSF is known exactly, we compare the Hotelling observer to other observers commonly used for planet detection; comparison is based on receiver operating characteristic (ROC) and localization ROC (LROC) curves.
 Barrett, H. H., Myers, K. J., Devaney, N., Dainty, J. C., & Caucci, L. (2006). Task Performance in Astronomical Adaptive Optics. Proceedings of SPIEthe International Society for Optical Engineering, 6272, 62721W.More infoIn objective or taskbased assessment of image quality, figures of merit are defined by the performance of some specific observer on some task of scientific interest. This methodology is well established in medical imaging but is just beginning to be applied in astronomy. In this paper we survey the theory needed to understand the performance of ideal or ideallinear (Hotelling) observers on detection tasks with adaptiveoptical data. The theory is illustrated by discussing its application to detection of exoplanets from a sequence of shortexposure images.
Proceedings Publications
 Caucci, L. (2020, Jan). Comparing training variability of CNN and optimal linear data reduction on image textures. In IS&T International Symposium on Electronic Imaging.
 Caucci, L. (2019, Oct). A Real Time Adaptive Strategy for Gamma Ray Imaging Systems. In IEEE NSSMIC Conference.
 Caucci, L., Barrett, H. H., Ding, J., & Henscheid, N. (2017, Oct 2017). Particleprocessing detectors for chargedparticle emission tomography. In IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC).
 Caucci, L., & Furenlid, L. R. (2015, August). Graphics Processing units for biomedical imaging. In SPIE, 9594, 95940G.
 Caucci, L., Barrett, H. H., Liu, Z., Han, H., & Furenlid, L. R. (2015, September). Towards ContinuousToContinuous 3D Data Reconstruction. In 13th International Meeting on Fully ThreeDimensional Image Reconstruction in Radiology and Nuclear Medicine.
 Barrett, H. H., Myers, K. J., Caucci, L., Gregory, G., & Davis, A. (2013, 2014). RADIANCE AND PHOTON NOISE: Imaging in geometrical optics, physical optics, quantum optics and radiology. In NOVEL OPTICAL SYSTEMS DESIGN AND OPTIMIZATION XVII, 9193.
 Caucci, L., Hunter, W. C., Furenlid, L. R., Barrett, H. H., & , . (2010, 2010). Listmode MLEM Image Reconstruction from 3D ML Position Estimates. In 2010 IEEE NUCLEAR SCIENCE SYMPOSIUM CONFERENCE RECORD (NSS/MIC), 26432647.
 Burke, D., Devaney, N., Gladysz, S., Barrett, H. H., Whitaker, M. K., Caucci, L., Hubin, N., Max, C., & Wizinowich, P. (2009, 2008). Optimal Linear Estimation of Binary Star Parameters. In ADAPTIVE OPTICS SYSTEMS, PTS 13, 7015.
 Caucci, L., Furenlid, L. R., Barber, H., Furenlid, L., & Roehrig, H. (2008, 2015). GPU programming for biomedical imaging. In MEDICAL APPLICATIONS OF RADIATION DETECTORS V, 9594.
 Caucci, L., Jha, A. K., Furenlid, L. R., Clarkson, E. W., Kupinski, M. A., Barrett, H. H., & , . (2007, 2013). Image Science with PhotonProcessing Detectors. In 2013 IEEE NUCLEAR SCIENCE SYMPOSIUM AND MEDICAL IMAGING CONFERENCE (NSS/MIC).
 Lawson, P. R., Poyneer, L., Barrett, H., Frazin, R., Caucci, L., Devaney, N., Furenlid, L., Gladysz, S., Guyon, O., Krist, J., Maire, J., Marois, C., Mawet, D., Mouillet, D., Mugnier, L., Pearson, I., Perrin, M., Pueyo, L., Savransky, D., , Ellerbroek, B., et al. (2006, 2012). On Advanced Estimation Techniques for Exoplanet Detection and Characterization Using Groundbased Coronagraphs. In ADAPTIVE OPTICS SYSTEMS III, 8447.
Presentations
 Caucci, L., Henscheid, N., & Barrett, H. H. (2020, Apr 2020). Quantifying task performance with photonprocessing detectors. Annual Meeting of the American Association for Cancer Research. Virtual due to COVID: American Association for Cancer Research.
 Caucci, L. (2019, Fall 2019). Synthetic Imaging Systems. College of Medicine Data Blitz Seminar. Tucson, AZ: College of Medicine.
Other Teaching Materials
 Caucci, L. (2020. HighPerformance Computing for Medical Imaging on Graphics Processing Units (GPU) with CUDA. SPIE.More infoShort course given at the SPIE Medical Imaging Conference, 1520 February 2020, Houston, TX.
Others
 Caucci, L. (2020, Nov). Contractinggrid Search Algorithm on CPU, GPU and FPGA. github.com. https://github.com/caucci/get_data_proj/blob/master/summary.pdf