Keywords: MR Fingerprinting, MR-Guided Radiotherapy
Motivation: Abdominal four-dimensional (4D) magnetic resonance fingerprinting (MRF) shows great potential for benefiting image-guided radiotherapy (IGRT). However, the long reconstruction time hinders its clinical IGRT.
Goal(s): This study aims to develop an ultra-fast deep learning 4D-MRF (UFDL-4DMRF) reconstruction method for benefiting clinical IGRT.
Approach: The proposed UFDL-4DMRF method integrates motion compensation strategy into 4DMRF reconstruction using a three-stage deep-learning method. Twenty patients diagnosed with Hepatocellular Carcinoma were involved for training and inference.
Results: UFDL-4DMRF can reduce reconstruction time by 24-fold while maintaining comparable performance in image quality, tissue property accuracy, tumor-to-tissue contrast, and motion tracking to the most advanced conventional method.
Impact: UFDL-4DMRF will provide clinicians with new insights into the advantages of 4D-MRF for RT. Future work will focus on developing self-supervised deep learning models to overcome the limitations posed by the absence of ground truth data.
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