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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