The Graduate Programs in Medical Physics at the University of Chicago offers research training that leads to the Doctor of Philosophy degree as well as postdoctoral training. Students working toward a graduate degree in medical physics are expected to have completed training equivalent to that required for the S.B. degree in the Department of Physics at this University. Postdoctoral trainees are selected from candidates with the Ph.D. degree in Physics or equivalent fields. Primary areas of research interests by the program faculty include four components: Physics of Diagnostic Radiology, Physics of Nuclear Medicine, Physics of Magnetic Resonance Imaging/Spectroscopy, and Physics of Radiation Therapy. Unique features of this program are the faculty's focused effort on research in medical imaging and radiation oncology, and on the training of high-level medical physicists. Trainees are required to take course work, participate in seminars and journal club meetings, assist in research projects, and complete research under supervision of a faculty member. Research projects may be theoretical or experimental studies in digital radiography, diagnostic performance, computer-aided diagnosis, magnetic resonance imaging and spectroscopy, nuclear medicine imaging, positron emission tomography, computer applications in radiation therapy, dose computation and verification, multi-modality image correlation, or dosimetry. All trainees take a cancer and radiation biology courses, participate in programs related to responsible conduct of research, and serve as teaching assistants. The number of current program faculty is 22. The number of current trainees includes 30 pre-doctoral students and six post-doctoral trainees. The number of trainees for which funding is requested is eight per year at the pre-doctoral level (4 first-year and 4 second-year trainees per year), and 2 per year postdoctoral level. It should be noted that this is a competitive renewal application, written at the end of the 19th year of the medical physics training grant that initiated in NCI and transferred to NIBIB, which has funded the program since 2003 including the last successful NIBIB competitive renewal review and 5-year funding period of Yrs 16-20.
The increasing use of biomedical imaging, image-guided interventions, and radiation therapies in medicine and the increasing interest in medical research serve to increase the demand for medical physicists. Thus, there is a clear need for training of medical physicists. This is the rationale for our proposed research training program.
|Haddad, Christopher W; Drukker, Karen; Gullett, Rebecca et al. (2018) Fuzzy c-means segmentation of major vessels in angiographic images of stroke. J Med Imaging (Bellingham) 5:014501|
|Anthony, Gregory J; Bader, Kenneth B; Wang, James et al. (2018) MRI-guided transurethral insonation of silica-shell phase-shift emulsions in the prostate with an advanced navigation platform. Med Phys :|
|Grelewicz, Zachary; Belcher, Andrew H; Wiersma, Rodney D (2018) Use of a laser-guided collimation system to perform direct kilovoltage x-ray spectra measurements on a linear accelerator onboard imager. Med Phys 45:4869-4876|
|Mendel, Kayla; Li, Hui; Sheth, Deepa et al. (2018) Transfer Learning From Convolutional Neural Networks for Computer-Aided Diagnosis: A Comparison of Digital Breast Tomosynthesis and Full-Field Digital Mammography. Acad Radiol :|
|Bader, Kenneth B (2018) The influence of medium elasticity on the prediction of histotripsy-induced bubble expansion and erythrocyte viability. Phys Med Biol 63:095010|
|Mendel, Kayla R; Li, Hui; Lan, Li et al. (2018) Quantitative texture analysis: robustness of radiomics across two digital mammography manufacturers' systems. J Med Imaging (Bellingham) 5:011002|
|Anthony, Gregory J; Cunliffe, Alexandra; Castillo, Richard et al. (2017) Incorporation of pre-therapy 18 F-FDG uptake data with CT texture features into a radiomics model for radiation pneumonitis diagnosis. Med Phys 44:3686-3694|
|Antropova, Natalia; Huynh, Benjamin Q; Giger, Maryellen L (2017) A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets. Med Phys 44:5162-5171|
|Liu, Xinmin; Pelizzari, Charles; Belcher, Andrew H et al. (2017) Use of proximal operator graph solver for radiation therapy inverse treatment planning. Med Phys 44:1246-1256|
|Wu, Yicong; Kumar, Abhishek; Smith, Corey et al. (2017) Reflective imaging improves spatiotemporal resolution and collection efficiency in light sheet microscopy. Nat Commun 8:1452|
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