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Abstract #1080

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

Lu Wang1, Chenyang Liu1, Shaohua Zhi1, Ge Ren1, Tian Li1, Peng Cao2, and Jing Cai1
1The Hong Kong Polytechnic University, Hong Kong, China, 2The University of Hong Kong, Hong Kong, China

Synopsis

Keywords: MR Fingerprinting, Radiotherapy

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.

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Keywords