Imaging and Precision Radiotherapy
The overall goal of this research is to improve the understanding, diagnosis, and treatment of cancer through preclinical and clinical investigations and translation of novel forms of cancer imaging, radiobiologic agents, and precision radiotherapy.
- Amit Sawant, PhD, Associate Professor
- Arezoo Modiri, PhD, Instructor
- Pouya Sabouri, PhD, Postdoctoral Fellow
- Mahdi Hamzeei, PhD, Postdoctoral Fellow
- Mohammad Tajdini, PhD, Postdoctoral Fellow
- Maida Ranjbar, MS, Research Associate
- Justin Cohen, Research Assistant
Major Research Areas
Small-Animal Image-Guided Radiotherapy
One of the most exciting developments in preclinical radiotherapy research has been the introduction and increased adoption of small-animal image-guided radiotherapy (SA-IGRT) platforms. Compared to traditional preclinical irradiation systems, which have rudimentary dose delivery capabilities with little or no treatment planning capabilities, SA-IGRT systems are designed with features that are routinely used in clinical radiotherapy: 3D imaging, inverse planning, and advanced dose delivery. Our group has several research programs developed around the SARRP system (Xstrahl Ltd), with the overarching goal of translating preclinical findings into future clinical studies. Our major areas of research include:
- Development of orthotopic tumor models in mice and rats for prostate, lung, and pancreatic cancer;
- Investigations of thermally modulated radiotherapy to sensitize tumor tissue and protect normal tissue (thereby widening the therapeutic window); and
- Investigations of advanced imaging techniques to spatially map postradiotherapy inflammation in lung stereotactic body radiotherapy.
Next-Generation Motion Management
Accounting for temporal anatomic changes in thoracic and abdominal cancer radiotherapy is one of the key scientific and clinical challenges of our era. These anatomic changes reduce image quality and targeting accuracy, which in turn leads to geometric and dosimetric errors in treatment. Such errors become increasingly important as we move toward hypofractionated regimens, such as stereotactic ablative radiotherapy, where highly potent doses are administered to the tumor target in a few fractions. Thoracic anatomy changes in all four dimensions (4D = 3D + time) from cycle to cycle and day to day. A common limitation of current motion management techniques is that they discard large amounts of this 4D information and neither capture nor adequately account for cycle-to-cycle variations. The goal of our motion management research is to capture and account for all four dimensions at each radiotherapy step: simulation, treatment planning, and dose delivery. We use high-performance CPU and GPU computing to:
- Create patient-specific, parameterized volumetric motion models that describe the underlying patient anatomy as a function of optical surface over many breathing cycles;
- Investigate higher-order inverse planning strategies using GPU-based swarm optimization algorithms to create truly 4D-optimized treatment plans that use motion as an additional degree of freedom in the optimization process; and
- Track real-time motion using a dynamic multileaf collimator that reshapes the beam to follow all of the complex changes (translation, rotation, and deformation) of the tumor and surrounding organs.