Radiotherapy is one of the most effective and commonly used modalities for cancer treatment. Radiotherapy uses high-energy radiation to eradicate cancer cells while sparing the surrounding normal tissue as much as possible. However, if unaccounted for, internal organ motion during radiation delivery may lead to underdosing of cancer cells or overdosing of normal tissue. Organ motion is of particular concern in the treatment of lung and abdominal cancers, where breathing induces large tumor displacement and organ deformation. A recent technological innovation is a new generation of radiotherapy systems equipped with on-board magnetic resonance imaging (MRI) scanners providing a real-time high-contrast movie of the patient's anatomy during radiation delivery. This research will develop the methods to enable use of real-time imaging to control the progress of radiation delivery in order to correct for any dose discrepancy, thus allowing treatment plans to actively adjust to anatomical changes. If successful, the research will provide clinicians with real-time organ-motion management tools that have the potential to improve the delivery efficiency and radiation dose conformity for MRI-guided radiotherapy, leading to higher rates of disease control and fewer side effects for cancer patients, thus improving their quality of life after treatment. The project will provide support for a graduate student who will benefit from the multi-disciplinary research environment.
This research employs medical-image processing techniques to infer the time-dependent state of the patient's anatomy from the stream of MRI sequences and to estimate the cumulative delivered dose in real-time. Using the inferred anatomical information, a stochastic model will be developed to project the immediate future trajectory of the anatomical motion. The cumulative dose estimation and the projected anatomical trajectory will be integrated as feedback and feed-forward control signals, respectively, into a stochastic control problem, which will be solved dynamically to (re)optimize the radiotherapy plan over the remainder of the treatment session. The control approach will be tested on clinical cancer patients previously treated with MRI-guided radiotherapy at a collaborating cancer center to quantify the anticipated improvement in plan quality and delivery efficiency. The project also provides a foundation from which to quantify the value of real-time MRI information.
