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Abstract #3613

Super-resolution MRI using deep convolutional neural network for adaptive MR-guided radiotherapy: a pilot study   

Yihang Zhou1, Hongyu Li2, Jing Yuan1, Leslie Ying2, Kin Yin Cheung1, and Siu Ki Yu1
1Medical Physics & Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, China, 2Department of Biomedical Engineering, Department of Electrical Engineering, The State University of New York at Buffalo, Buffalo, NY, United States

MR-guided radiotherapy (MRgRT) is creating new perspectives towards an individualized precise radiation therapy solution. However, spatial resolution of fractional MRI can be much restricted, in order to shorten scan time, by patient tolerance of immobilization, intra-fractional anatomical motion and complicated MRgRT workflow. We hypothesized that the quality of low-resolution daily MRI could be greatly restored to generate super-resolution MRI, whose quality should be comparable of high-resolution planning MRI, by applying deep learning techniques. In this study, we aimed to investigate the feasibility of deep learning super-resolution MRI generation in the head-and-neck for adaptive MRgRT purpose.

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