Adaptive Gate-Aware Mamba Networks for Magnetic Resonance Fingerprinting
Journal:
arXiv
Published Date:
Jul 4, 2025
Abstract
Magnetic Resonance Fingerprinting (MRF) enables fast quantitative imaging by
matching signal evolutions to a predefined dictionary. However, conventional
dictionary matching suffers from exponential growth in computational cost and
memory usage as the number of parameters increases, limiting its scalability to
multi-parametric mapping. To address this, recent work has explored deep
learning-based approaches as alternatives to DM. We propose GAST-Mamba, an
end-to-end framework that combines a dual Mamba-based encoder with a Gate-Aware
Spatial-Temporal (GAST) processor. Built on structured state-space models, our
architecture efficiently captures long-range spatial dependencies with linear
complexity. On 5 times accelerated simulated MRF data (200 frames), GAST-Mamba
achieved a T1 PSNR of 33.12~dB, outperforming SCQ (31.69~dB). For T2 mapping,
it reached a PSNR of 30.62~dB and SSIM of 0.9124. In vivo experiments further
demonstrated improved anatomical detail and reduced artifacts. Ablation studies
confirmed that each component contributes to performance, with the GAST module
being particularly important under strong undersampling. These results
demonstrate the effectiveness of GAST-Mamba for accurate and robust
reconstruction from highly undersampled MRF acquisitions, offering a scalable
alternative to traditional DM-based methods.
