Physics informed guided diffusion for accelerated multi-parametric MRI reconstruction
Journal:
arXiv
Published Date:
Jun 29, 2025
Abstract
We introduce MRF-DiPh, a novel physics informed denoising diffusion approach
for multiparametric tissue mapping from highly accelerated, transient-state
quantitative MRI acquisitions like Magnetic Resonance Fingerprinting (MRF). Our
method is derived from a proximal splitting formulation, incorporating a
pretrained denoising diffusion model as an effective image prior to regularize
the MRF inverse problem. Further, during reconstruction it simultaneously
enforces two key physical constraints: (1) k-space measurement consistency and
(2) adherence to the Bloch response model. Numerical experiments on in-vivo
brain scans data show that MRF-DiPh outperforms deep learning and compressed
sensing MRF baselines, providing more accurate parameter maps while better
preserving measurement fidelity and physical model consistency-critical for
solving reliably inverse problems in medical imaging.
