The Radiological Sciences Training Grant supports Ph.D. and postdoctoral research related to cancer. The goal of this training program is to prepare physicists and engineers for research careers in radiological physics and dosimetry as well as in functional, anatomical, and interventional medical imaging as it applies to the detection and treatment of cancer.
Faculty trainers in this program are committed to excellence in research broadly applied to cancer treatment, diagnosis, and prevention. We take a multi-disciplinary approach that is strongly image-science based and is increasingly molecular-focused. Trainees are immersed in this comprehensive research environment. Research training is available in every major area of physics involved with cancer treatment and diagnosis, cancer biology, and in emerging areas of molecular imaging.
The Radiological Sciences Training grant supports 8 predoctoral trainees and 3 postdoctoral trainees. About 4-5 training grant openings are filled each year to replace individuals rotating off because of graduation or completion of training. Nominations for training grant positions are made by either the student’s advisor or the faculty advisor/sponsor in the case of postdoctoral nominations. Nominations include a description of the area of research, the cancer relevance, and an agreement that the student will fulfill the requirements of the training grant, detailed below. In most cases students must have reached dissertator status to be considered for a training grant position. To assure that a broad research area is included amongst trainees, faculty members will generally not have more than one advisee on the grant.
More details on the requirements for trainees can be found in the T32 Trainee Handbook.
- Thesis research must be focused on the broad area of diagnosis, treatment, or treatment monitoring of cancer
- Trainees must have had, or be enrolled in, a course in cancer biology
- Trainees must have had, or be enrolled in, a course in research ethics training
- Trainees must participate in the annual Radiological Physics Training Grant symposium; in addition, trainees must prepare a progress report each spring.
- Authors of papers and theses must acknowledge partial support of the training grant in their publications
- Appointments cannot exceed 5 years for predoctoral trainees and 3 years for postdoctoral trainees. Because of previous courses taken and research work completed, most predoctoral dissertators complete their work in 2 years.
- Postdoctoral training includes preparing a K-series or an R series NIH grant application
Each publication, press release, or other document about research supported by an NIH award must include an acknowledgment of NIH award support and a disclaimer such as:
“Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under Award Number T32CA009206. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.”
Prior to issuing a press release concerning the outcome of this research, please notify the NIH awarding IC in advance to allow for coordination.
Current Trainees and their Projects
|Dr. Jose Guerrero Gonzalez||Dr. Andrew Alexander||Multidimensional diffusion MRI with multivariate analyses for the investigation of tissue microscopic heterogeneity in brain cancer tumors||11/1/2020|
|Dr. Jessica Hsu||Dr. Weibo Cai||Targeted, X-ray responsive nanoparticles for improved RT planning and treatment efficacy in breast cancer||8/1/2022|
|Kaelyn Becker||Dr. Jon Engle||Production of 43/44Sc from isotopically enriched calcium targets to achieve clinical yields and quality||9/1/2021|
|Collin Buelo||Dr. Diego Hernando||Development of a Deep Learning-based Quantitative Susceptibility Mapping Method for Liver Iron Quantification||9/1/2021|
|Emily Shelton||Dr. Paul Campagnola||Developing a combined theoretical and experimental approach to use the SHG spatial emission pattern of SHG to investigate the collagen fibril assembly in ovarian cancer||2/1/2022|
|Nicholas Summerfield||Dr. Carri Glide-Hurst||Implementing cutting-edge deep learning techniques to cardiac segmentation, namely implementing a first-of-kind UNEt+TRansformer (UNETR) algorithm and applying the techniques to a cancer patient cohort||9/1/2022|
|Kevin Treb||Dr. Ke Li||Development of a C-arm photon counting CT prototype system for image-guided interventional treatments of liver cancer||9/1/2021|
|Joseph Whitehead||Dr. Michael Speidel||Development of a low dose, motion-compensated, quantitative x-ray imaging technique to assist with standardization of clinical endpoints during hepatic embolization procedures||7/1/2021|
|Muhsin Younis||Dr. Weibo Cai||To develop novel techniques/agents for imaging and therapy of cancer and other diseases||8/1/2022|