Design and optimization of medical devices for MRI-guided radiotherapy applications

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Over 200,000 new cases of cancer are expected in Canada in 2020 with radiation therapy (RT) playing a pivotal role in the management of the disease in nearly half of the cases. RT is a balance between accurately treating the target tumour with sufficiently lethal dose of radiation while minimizing the dose to surrounding healthy tissue. Image-guided RT greatly improves our ability to make treatments increasingly conformal, improving this therapeutic ratio, and adapting treatment to changes in tumour shape and size over the course of treatment. Advances in medical linear accelerators have provided mm-level accuracies in delivering dose: imaging system performance is now the limiting factor in conformal dose delivery and providing improved care. Magnetic resonance (MR) imaging provides superior soft-tissue contrast to better distinguish healthy tissue from cancerous tumours and has been increasingly used for diagnosis, treatment planning and more recently in-room guidance for RT treatment delivery. Recently, novel MR-Linac technologies have been built on this concept. However, MR is a complex imaging modality that requires both advanced technical and clinical expertise to ensure its effectiveness and safety. It is recognized that MR images intrinsically suffer of geometric distortions due to both scanner specifications and performance as well as patient-induced magnetic field effects. Our program aims to reduce these uncertainties in RT planning and guidance by developing new-generation devices for the quantification of MR image quality (MR-IQ), by defining new theoretical and experimental methodologies. The aims of the research program are (1) to develop fundamental methods, design, and test MRID3D with extended field of view using harmonic analysis and magnetic susceptibility simulations; (2) to model MRID3D with IQ inserts to design a comprehensive device for the management of MR-IQ; (3) to develop an automated MR-IQ image processing pipeline powered by machine learning methodology. The impact of this program is to provide overall better consistency, conceptual clarity, and enhanced accuracy to the MR-IQ relevant to radiation therapy applications. Improved efficiency and accuracy for comprehensive MR-IQ in the long term will lead to a wider acceptance and deeper integration of MR technologies in RT, and implicitly will contribute to better outcomes. Our program builds on a track record of practice-changing MR-IQ with some of our technologies available to end-users worldwide for testing and clinical implementation.