Harrison H Barrett
- Regents Professor Emeritus
- Regents Professor Emeritus
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 Gamma-ray Imaging, an NIH-funded research resource that develops state-of-the 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.
- 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
- 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)
- 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
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 Solid-state 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)
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 maximum-likelihood methodsCharged-particle imaging Digital radiology and telemedicine in remote regionsRadiation dose, patient risk and image qualityQuantum imaging
Image Science for OncologyOPTI 639 (Fall 2019)
Intro to Image ScienceOPTI 536 (Spring 2019)
Image Science for OncologyOPTI 639 (Fall 2018)
DissertationMATH 920 (Spring 2018)
Intro to Image ScienceOPTI 536 (Spring 2018)
DissertationMATH 920 (Fall 2017)
ThesisOPTI 910 (Fall 2017)
DissertationMATH 920 (Spring 2017)
DissertationMATH 920 (Fall 2016)
DissertationPHYS 920 (Fall 2016)
DissertationMATH 920 (Spring 2016)
DissertationPHYS 920 (Spring 2016)
Intro to Image ScienceOPTI 536 (Spring 2016)
- Barrett, H. H., & Caucci, L. (2021). Erratum: Publisher's Note: Stochastic models for objects and images in oncology and virology: application to PI3K-Akt-mTOR signaling and COVID-19 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), 3311-3323.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.
- Barrett, H. H., Caucci, L., & Ding, Y. (2017). Charged-particle emission tomography. Medical Physics.
- 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 infinite-dimensional 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 maximum-likelihood estimation with Emission Computed Tomography (ECT) data to estimate unknown patient-specific 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). Charged-particle emission tomography. Medical physics, 44(6), 2478-2489.More infoConventional charged-particle imaging techniques - such as autoradiography - provide only two-dimensional (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 three-dimensional (3D) autoradiography technique, which we call charged-particle 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 charged-particle imaging with a window chamber or an endoscope.
- Ding, Y., Caucci, L., & Barrett, H. H. (2017). Null functions in three-dimensional imaging of alpha and beta particles. Scientific Reports, 7(1), 15807.
- Ding, Y., Caucci, L., & Barrett, H. H. (2017). Null functions in three-dimensional 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. particle-processing (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 Charged-Particle 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 normal-tissue complications as the overall radiation dose level is varied, e.g., by varying the beam current in external-beam 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 patient-specific 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, 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.
- 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 photon-limited 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 photon-processing detectors (capable of measuring radiance on a photon-by-photon 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 Monte-Carlo 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 photon-processing data.
- 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.