Physics informed guided diffusion for accelerated multi-parametric MRI reconstruction

• URL: http://arxiv.org/abs/2506.23311v1
• Date: Sun, 29 Jun 2025 16:00:32 GMT
• Title: Physics informed guided diffusion for accelerated multi-parametric MRI reconstruction
• Authors: Perla Mayo, Carolin M. Pirkl, Alin Achim, Bjoern Menze, Mohammad Golbabaee,
• Abstract summary: We introduce MRF-DiPh, a physics informed denoising diffusion approach for multiparametric tissue mapping.<n> Numerical experiments on in-vivo brain scans data show that MRF-DiPh outperforms deep learning and compressed sensing MRF baselines.
• Score: 4.833916353245013
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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