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This seminar series is designed for students interested in biomedical physics, radiation therapy, image-guided therapy, diagnostic, interventional, and molecular imaging, and other forms of disease detection and characterization including molecular diagnostics. The course will give an overview of research laboratories and projects occurring in Radiology and Radiation Oncology. Speakers include Stanford faculty and research scientists, as well as industry professionals.

Physics and Engineering Principles of Multi-modality Molecular Imaging of Living Subjects ( RAD 222A). Focuses on instruments, algorithms and other technologies for non-invasive imaging of molecular processes in living subjects. Introduces research and clinical molecular imaging modalities, including PET, SPECT, MRI, Ultrasound, Optics, and Photoacoustics. For each modality, lectures cover the basics of the origin and properties of imaging signal generation, instrumentation physics and engineering of signal detection, signal processing, image reconstruction, image data quantification, applications of machine learning, and applications of molecular imaging in medicine and biology research.

This course covers concepts of radiological physics and dosimetry that are fundamental for understanding and exploring the most common medical application of physics in imaging and therapy. The course comprises two major parts. Part I focuses on the interaction of ionizing radiation with matter as the basis for understanding radiation/human body interactions. Part II focuses on radiation measurement devices and techniques for accurate assessment of imaging and therapeutic doses of radiation. Prerequisites: Undergraduate degree in Physics, Engineering, or other closely related scientific discipline. Students with non-Physics backgrounds must have had a coursework satisfying the requirements for an undergraduate minor in Physics. This includes the calculus based introductory general physics sequence and three upper division (junior or senior level) topical physics courses that would be taken by a physics major. Examples of upper division courses include Modern Physics or Electricity and Magnetism.

Data-driven biomedical imaging and physics is an emerging interdisciplinary field that combines advanced medical physics concepts, deep learning algorithms, and biomedical imaging technologies to develop new approaches for diagnosis, treatment, and research in the biomedical field. The main goal of this course is to provide background knowledge of biomedical imaging and physics, introduce the fundamentals of deep learning and data-driven techniques, describe the problems and data-driven solutions in imaging and medical physics, and present clinical use cases and successful examples in data-driven biomedical physics. It is anticipated that the students will gain useful knowledge and practical skills to advance the field of data-driven biomedical imaging and physics in the near future.

Students undertake investigations sponsored by individual faculty members. Prerequisite: consent of instructor.