MRI super‐resolution reconstruction for MRI‐guided adaptive radiotherapy using cascaded deep learning: In the presence of limited training data and unknown translation model
Medical Physics2019Vol. 46(9), pp. 4148–4164
Citations Over TimeTop 10% of 2019 papers
Jaehee Chun, Hao Zhang, H. Michael Gach, Sven Olberg, Thomas R. Mazur, Olga Green, Taeho Kim, Hyun Kim, Jin Sung Kim, Sasa Mutic, Justin C. Park
Abstract
The robust, SR reconstruction method for MRI based on the novel cascaded deep learning framework is an end-to-end method for producing detail-preserving SR reconstructions from noisy, LR clinical MRI scans. Fourfold enhancements in spatial resolution facilitate target delineation and motion management during radiation therapy, enabling precise MRI-guided radiation therapy with 3D LR breath-hold MRI and 4D-MRI in a clinically feasible time frame.
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