Proximal Gradient Decent Network for Respiratory-Correlated Four-Dimensional Abdominal MR Fingerprinting Reconstruction (PGDN-RC-4DMRF)

Motivation: Four-dimensional MR fingerprinting (4D-MRF) has the potential to improve precision and efficacy in abdominal radiotherapy (RT). However, the long reconstruction time of the state-of-the-art reconstruction method, compress-sensed-based respiratory-correlated 4D-MRF (CS-RC-4DMRF), limits its clinical application. Goal(s): The study aims to develop a novel method to reduce the reconstruction time of CS-RC-4DMRF. Approach: We developed PGDN-RC-4DMRF by integrating a deep proximal gradient descent network (PGDN) into 4D-MRF. Tumor motion tracking accuracy, tissue quantification accuracy, and image quality were evaluated. Results: The proposed PGDN-RC-4DMRF method reduce the reconstruction time by a factor of 120, decreasing it from 8 min/slice to 4 s/slice while maintaining other metrics. Impact: The improvement in the reconstruction speed of 4D-MRF through PGDN-RC-4DMRF may enhance the practicality of 4D-MRF in clinical settings for clinicians and potentially benefit RT outcomes for patients.