A deep learning approach for fast muscle water T2 mapping with subject specific fat T2 calibration from multi-spin-echo acquisitions.

ABSTRACT: This work presents a deep learning approach for rapid and accurate muscle water T₂ with subject-specific fat T₂ calibration using multi-spin-echo acquisitions. This method addresses the computational limitations of conventional bi-component Extended Phase Graph fitting methods (nonlinear-least-squares and dictionary-based) by leveraging fully connected neural networks for fast processing with minimal computational resources. We validated the approach through in vivo experiments using two different MRI vendors. The results showed strong agreement of our deep learning approach with reference methods, summarized by Lin's concordance correlation coefficients ranging from 0.89 to 0.97. Further, the deep learning method achieved a significant computational time improvement,

SUBMITTER: Barbieri M 

PROVIDER: S-EPMC11002020 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature