Physics informed guided diffusion for accelerated multi-parametric MRI reconstruction

2025/06/29 by Mayo, Perla, Pirkl, Carolin M., Achim, Alin +2
#FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Physical sciences #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Medical Physics (physics.med-ph) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2506.23311

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.

Citations

• Denoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprinting
• Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction
• Self-Supervised MRI Reconstruction with Unrolled Diffusion Models
• Denoising Diffusion Models for Plug-and-Play Image Restoration
• DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction
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• Robust Compressed Sensing MRI with Deep Generative Priors
• Diffusion Models Beat GANs on Image Synthesis
• Improved Denoising Diffusion Probabilistic Models
• Score-Based Generative Modeling through Stochastic Differential Equations
• Denoising Diffusion Implicit Models
• Plug-and-Play Image Restoration with Deep Denoiser Prior
• Denoising Diffusion Probabilistic Models
• Rapid three-dimensional multiparametric MRI with quantitative\n transient-state imaging
• CoverBLIP: accelerated and scalable iterative matched-filtering for Magnetic Resonance Fingerprint reconstruction
• SVD Compression for Magnetic Resonance Fingerprinting in the Time Domain
• A Compressed Sensing Framework for Magnetic Resonance Fingerprinting

Related

• Denoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprinting
• Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting
• Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction
• A Plug-and-Play Approach to Multiparametric Quantitative MRI: Image Reconstruction using Pre-Trained Deep Denoisers
• High-Frequency Space Diffusion Model for Accelerated MRI