Spatio-temporal motion prediction model for liver cancer radiotherapy

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Free-breathing liver cancer therapies such as external beam radiation therapy (EBRT) require accurate tumor tracking within an organ which deforms and moves during treatment. However, a major limitation of tumor-targeted interventions resides in the patient's respiration or involuntary movement, which may stray the pre-defined target and trajectories determined during planning from the actual anatomy, thus inducing errors in the relative position of the therapy device performing the action with respect to the target. Live motion tracking of the internal anatomy depends on 3D imaging and image post-processing in real-time, which is unfeasible during interventional procedures. Thus, to complete partial information (2D images, navigator signal) clinically available during treatment, prior knowledge of the anticipated motion field during the breathing cycle is necessary. The project objective is to develop and evaluate a spatio-temporal liver motion model based on deep learning and biomechanical models, which will predict tumor displacement within the breathing cycle and help radiation oncologists not only track tumors, but also avoid damaging critical structures surrounding the tumor target. This project will have significant direct and indirect economic benefits. As to the direct benefits, the primary industrial Partner - Elekta - who is an innovator of equipment and software for radiation therapy and radiosurgery, will have access to 4D-MRI imaging data and computerized models based on machine learning, which will address their critical needs for motion management during therapy. The outcome will be particularly beneficial with their MR-Linac system, developed in partnership with Philips Healthcare, in order to improve targeting accuracy and minimize organ deformation artefacts during radiation. Indirect benefits include better patient care and reduced number of required interventions. This multidisciplinary translational research program will provide a unique opportunity for 2 graduate students, 3 undergraduate students and one post-doctoral fellow in the field of biomedical sciences to work with experts in medical imaging and pathology and for medical students, residents and fellows to translate fundamental concepts in the clinical field.