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Magnetic Resonance Imaging (MRI) is a non-invasive diagnostic andradiotherapy (RT) planning tool, offering detailed insights into the anatomy ofthe human body. The extensive scan time is stressful for patients, who mustremain motionless in a prolonged imaging procedure that prioritizes reductionof imaging artifacts. This is challenging for pediatric patients who mayrequire measures for managing voluntary motions such as anesthesia. Severalcomputational approaches reduce scan time (fast MRI), by recording fewermeasurements and digitally recovering full information via post-acquisitionreconstruction. However, most fast MRI approaches were developed for diagnosticimaging, without addressing reconstruction challenges specific to RT planning.In this work, we developed a deep learning-based method (DeepMRIRec) for MRIreconstruction from undersampled data acquired with RT-specific receiver coilarrangements. We evaluated our method against fully sampled data of T1-weightedMR images acquired from 73 children with brain tumors/surgical beds using loopand posterior coils (12 channels), with and without applying virtualcompression of coil elements. DeepMRIRec reduced scanning time by a factor offour producing a structural similarity score surpassing the evaluatedstate-of-the-art method (0.960 vs 0.896), thereby demonstrating its potentialfor accelerating MRI scanning for RT planning.

Deep-learning-based acceleration of MRI for radiotherapy planning of pediatric patients with brain tumors