MR-guided radiation therapy
Technological advancements have enabled the establishment of a radiotherapy workflow based entirely on magnetic resonance (MR) imaging. This innovation will unlock the full potential of MR, including various contrast mechanisms as well as physiological imaging, such as diffusion-weighted imaging, dynamic contrast-enhanced MR imaging. Physiological MR imaging offers the potential to improve the targeting as well as provide quantitative assessment of the target and normal tissue response to therapy. The combination of soft tissue anatomical change information and physiological MR image information will enable a new paradigm for MR-based adaptive radiation therapy that combines both anatomic and biologic adaptation. Impact of the system-wide integration of MR imaging into the radiation therapy process will include improved target and normal structure delineation for guidance, as well as physiological information for biologic adaptation of the radiation therapy delivery throughout the course of treatment.
Automated adaptive radiation therapy
Adaptive radiation therapy offers the ability to personalize treatment by improving the targeting of radiation delivery and minimizing the normal tissue toxicities in presence of anatomical changes and intra-fraction motion as assessed on cone-beam computed tomography or MR imaging. Increased utilization of adaptation in the clinic requires implementation of automated methods to track treatment delivery, assess need for adaption as well as execute steps to adapt radiation delivery. Automating adaptive radiation therapy will include training, validation, and continuous update of machine-learning methods for automated segmentation and treatment planning in the context of evolving clinical practice and advancements in imaging and treatment technology.
- Cancer Clinical Research Unit (CCRU), Princess Margaret Cancer Centre
- Medical Physicist, Radiation Medicine Program, Princess Margaret Cancer Centre
- Assistant Professor, Department of Radiation Oncology, University of Toronto