Biography
Dr. Barrett received a bachelor's degree in physics from Virginia Polytechnic Institute in 1960, a master's degree in physics from MIT in 1962, and a Ph.D. in applied physics from Harvard in 1969. He worked for the Raytheon Research Division until 1974, when he came to the University of Arizona. He is a professor in the College of Medicine and the College of Optical Sciences, and he has appointments in Applied Mathematics, Biomedical Engineering and the Arizona Cancer Center. In 1983 he served as acting director of the Optical Sciences Center, and in 1990 he was named a Regents Professor. He is a fellow of the Optical Society of America, the Institute of Electrical and Electronic Engineers, the American Physical Society and the American Institute of Medical and Biological Engineering. He has 25 U. S. patents and approximately 300 technical papers, and 58 students have received Ph.D. degrees under his direction. His awards include a Humboldt Prize, the 2000 IEEE Medical Imaging Scientist Award, an E. T. S. Walton Award from Science Foundation Ireland, and the 2005 C. E. K. Mees Medal from the Optical Society of America. He is the 2011 recipient of the IEEE Medal for Innovations in Healthcare Technology and also the 2011 recipient of the SPIE Gold Medal of the Society.
His current research is in image science, with applications in medicine and astronomy. He is director of the Center for Gammaray Imaging, an NIHfunded research resource that develops stateofthe art instruments for radiotracer studies of small animals. He is also active in developing new methods for the assessment and optimization of image quality and in applying parallel computers to imaging. In collaboration with Kyle J. Myers, he has written a book entitled Foundations of Image Science, which in 2006 was awarded the First Biennial J. W. Goodman Book Writing Award from OSA and SPIE.
Degrees
 Other Doctor Honoris Causa
 University of Ghent, Belgium
 Ph.D. Applied Physics
 Harvard University, Boston, Massachusetts
 M.S. Physics
 Massachusetts Institute of Technology, Cambridge, Massachusetts
 B.S. Physics
 Virginia Polytechnic Institute, Blacksbug, Virginia
Work Experience
 University of Arizona, Tucson, Arizona (2006  Ongoing)
 University of Arizona, Tucson, Arizona (2005  2009)
 University of Arizona, Tucson, Arizona (1990  Ongoing)
 University of Arizona, Tucson, Arizona (1988  Ongoing)
 University of Arizona, Tucson, Arizona (1986  Ongoing)
 University of Arizona, Tucson, Arizona (1983)
 University of Arizona, Tucson, Arizona (1976  1990)
 University of Arizona, Tucson, Arizona (1974  1976)
 Raytheon Research Division (1973  1974)
 Raytheon Research Division (1971  1974)
 Raytheon Research Division (1968  1973)
 Raytheon Research Division (1962  1968)
Awards
 Elected Fellow of SPIE
 SPIE, Fall 2016
 Paul C. Aebersold Award
 Society of Nuclear Medicine and Molecular Imaging, Summer 2014
 National Academy of Engineering
 National Academy of Engineering, Spring 2014
Interests
Teaching
HolographyOptical Data ProcessingRadiological ImagingAdvanced Radiological ImagingMathematical Methods for Optics (introductory level)Mathematical Methods for Optics (advanced level)Mathematical Optics LaboratoryQuantum OpticsSolid State OpticsStatistical OpticsQuantum and Solidstate OpticsElectromagnetic Theory for OpticsPrinciples of Image ScienceNoise in Imaging SystemsOptical ZingersPhysical OpticsIntroduction to Image ScienceImaging Physics and DevicesRadiometry, Sources and Detectors (graduate and undergraduate levels)
Research
Image ScienceSPECT, PET and CT imagingMolecular imaging in cancer and cardiovascular diseaseTheoretical and psychophysical investigations of image qualityApplications of parallel computing in imagingAstronomical imaging and adaptive opticsOptical metrology with maximumlikelihood methodsChargedparticle imaging Digital radiology and telemedicine in remote regionsRadiation dose, patient risk and image qualityQuantum imaging
Courses
201920 Courses

Image Science for Oncology
OPTI 639 (Fall 2019)
201819 Courses

Intro to Image Science
OPTI 536 (Spring 2019) 
Image Science for Oncology
OPTI 639 (Fall 2018)
201718 Courses

Dissertation
MATH 920 (Spring 2018) 
Intro to Image Science
OPTI 536 (Spring 2018) 
Dissertation
MATH 920 (Fall 2017) 
Thesis
OPTI 910 (Fall 2017)
201617 Courses

Dissertation
MATH 920 (Spring 2017) 
Dissertation
MATH 920 (Fall 2016) 
Dissertation
PHYS 920 (Fall 2016)
201516 Courses

Dissertation
MATH 920 (Spring 2016) 
Dissertation
PHYS 920 (Spring 2016) 
Intro to Image Science
OPTI 536 (Spring 2016)
Scholarly Contributions
Journals/Publications
 Barrett, H. H., & Caucci, L. (2021). Erratum: Publisher's Note: Stochastic models for objects and images in oncology and virology: application to PI3KAktmTOR signaling and COVID19 disease. Journal of medical imaging (Bellingham, Wash.), 8(Suppl 1), 019801.More info[This corrects the article DOI: 10.1117/1.JMI.8.S1.S16001.].
 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.
 Henscheid, N., Clarkson, E., Myers, K. J., & Barrett, H. H. (2018). Physiological random processes in precision cancer therapy. PloS one, 13(6), e0199823.More infoMany different physiological processes affect the growth of malignant lesions and their response to therapy. Each of these processes is spatially and genetically heterogeneous; dynamically evolving in time; controlled by many other physiological processes, and intrinsically random and unpredictable. The objective of this paper is to show that all of these properties of cancer physiology can be treated in a unified, mathematically rigorous way via the theory of random processes. We treat each physiological process as a random function of position and time within a tumor, defining the joint statistics of such functions via the infinitedimensional characteristic functional. The theory is illustrated by analyzing several models of drug delivery and response of a tumor to therapy. To apply the methodology to precision cancer therapy, we use maximumlikelihood estimation with Emission Computed Tomography (ECT) data to estimate unknown patientspecific physiological parameters, ultimately demonstrating how to predict the probability of tumor control for an individual patient undergoing a proposed therapeutic regimen.
 Henscheid, N., Meyers, K. J., Clarkson, E., & Barrett, H. H. (2017). Physiological random processes in precision cancer therapy. PLOS ONE.
 Ding, Y., Caucci, L., & Barrett, H. H. (2017). Chargedparticle emission tomography. Medical physics, 44(6), 24782489.More infoConventional chargedparticle imaging techniques  such as autoradiography  provide only twodimensional (2D) black ex vivo images of thin tissue slices. In order to get volumetric information, images of multiple thin slices are stacked. This process is time consuming and prone to distortions, as registration of 2D images is required. We propose a direct threedimensional (3D) autoradiography technique, which we call chargedparticle emission tomography (CPET). This 3D imaging technique enables imaging of thick tissue sections, thus increasing laboratory throughput and eliminating distortions due to registration. CPET also has the potential to enable in vivo chargedparticle imaging with a window chamber or an endoscope.
 Ding, Y., Caucci, L., & Barrett, H. H. (2017). Null functions in threedimensional imaging of alpha and beta particles. Scientific Reports, 7(1), 15807.
 Ding, Y., Caucci, L., & Barrett, H. H. (2017). Null functions in threedimensional imaging of alpha and beta particles. Scientific reports, 7(1), 15807.More infoNull functions of an imaging system are functions in the object space that give exactly zero data. Hence, they represent the intrinsic limitations of the imaging system. Null functions exist in all digital imaging systems, because these systems map continuous objects to discrete data. However, the emergence of detectors that measure continuous data, e.g. particleprocessing (PP) detectors, has the potential to eliminate null functions. PP detectors process signals produced by each particle and estimate particle attributes, which include two position coordinates and three components of momentum, as continuous variables. We consider ChargedParticle Emission Tomography (CPET), which relies on data collected by a PP detector to reconstruct the 3D distribution of a radioisotope that emits alpha or beta particles, and show empirically that the null functions are significantly reduced for alpha particles if ≥3 attributes are measured or for beta particles with five attributes measured.
 Barrett, H. H., Alberts, D. S., Woolfenden, J. M., Caucci, L., & Hoppin, J. W. (2016). Therapy operating characteristic curves: tools for precision chemotherapy. Journal of medical imaging (Bellingham, Wash.), 3(2), 023502.More infoThe therapy operating characteristic (TOC) curve, developed in the context of radiation therapy, is a plot of the probability of tumor control versus 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. This paper shows how 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. The mathematical analogy between response of observers to images and the response of tumors to distributions of a chemotherapy drug is exploited to obtain linear discriminant functions from which AUTOC can be calculated. Methods for using mathematical models of drug delivery and tumor response with imaging data to estimate patientspecific parameters that are needed for calculation of AUTOC are outlined. The implications of this viewpoint for clinical trials are discussed.
 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, 7286.More infoThe Fano factor of an integervalued 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 subPoisson, while CsI:Na and YAP:Ce were found to be superPoisson. 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 subPoisson, while YAP:Ce was found to be close to Poisson.
 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.
 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 (Redondo Beach, Calif.), 55(1).More infoThe statistics of detector outputs produced by an imaging system are derived from basic radiometric concepts and definitions. We show that a fundamental way of describing a photonlimited imaging system is in terms of a Poisson random process in spatial, angular, and wavelength variables. We begin the paper by recalling the concept of radiance in geometrical optics, radiology, physical optics, and quantum optics. The propagation and conservation laws for radiance in each of these domains are reviewed. Building upon these concepts, we distinguish four categories of imaging detectors that all respond in some way to the incident radiance, including the new category of photonprocessing detectors (capable of measuring radiance on a photonbyphoton basis). This allows us to rigorously show how the concept of radiance is related to the statistical properties of detector outputs and to the information content of a single detected photon. A MonteCarlo technique, which is derived from the Boltzmann transport equation, is presented as a way to estimate probability density functions to be used in reconstruction from photonprocessing data.
 Clarkson, E., & Barrett, H. H. (2016). Characteristic functionals in imaging and imagequality assessment: tutorial. Journal of the Optical Society of America. A, Optics, image science, and vision, 33(8), 146475.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 listmode 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.
 Barrett, H. H., Caucci, L., & Ding, Y. (2017). Chargedparticle emission tomography. Medical Physics.