The over-arching theme of this proposal is to train ?comprehensive imaging scientists? in the skills necessary to identify clinically relevant problems; develop instrumentation, sensors, and contrast agents to form images appropriate for the problem; and analyze the resulting imaging data using signal processing, mathematical modeling, visualization, and informatics techniques to improve the prevention, detection, diagnosis, and treatment of human diseases. The program spans from molecular to cellular to tissue to organ. In order for imaging scientists to be knowledgeable of the full trajectory from image formation to analysis and decision-making, they must be trained in four core areas: Instrumentation, Devices, and Contrast Agents; Image processing; Modeling and Visualization; and Data Mining and Informatics. All students in the program are trained in the core concepts of these areas. The current training program is a two-year pre-doctoral portfolio program. A total of 41 students have been admitted to the program. The proposed renewal will train another 20 students. The program includes off-campus externship research experiences; in-depth clinical engagement; and a wide-ranging professional development component. Imaging Science is an integral element of basic science research and clinical medicine. Imaging cell trafficking and receptor pharmacology in vivo have already led to targeted drug and gene therapies and an understanding of cellular biochemical pathways will contribute to new advances in medicine. Individualized medicine relies heavily on imaging techniques to select the best therapies and monitor progress. Although structural in situ human imaging is already a critical component of clinical medicine, many advances are needed in functional imaging of the brain and other organs to improve healthcare. Brain mapping which is a core focus of NIH research relies heavily on imaging. We have identified a critical need for imaging scientists to develop new imaging instrumentation and apply that instrumentation with appropriate methods from image processing; modeling and visualization; and informatics and data mining. In recognition of the potential of artificial intelligence to transform medical imaging, our program emphasizes applications of machine learning. This training program fills a critical niche by providing highly skilled scientists who are trained in the broad trajectory of imaging science. Understanding the interplay between instrumentation and image analysis, including machine learning methods, is important for designing the next generation of hardware and software tools for quantifying complex biological systems and providing robust clinical tools. A key outcome of the program is that trainees gain the skills necessary to identify clinically relevant problems.
The over-arching theme of this program is to train ?comprehensive imaging scientists? in the skills necessary to identify clinically relevant problems and develop techniques to improve the prevention, detection, diagnosis, and treatment of human diseases. Trainees learn the full trajectory of medical imaging, including the application of machine learning methods, and emphasis is placed on experiential learning in clinical environments. The global program outcome is for our students to acquire the skill set needed to improve healthcare through imaging science.
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