Physics informed guided diffusion for accelerated multi-parametric MRI reconstruction
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913917615210496 |
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| author | Mayo, Perla Pirkl, Carolin M. Achim, Alin Menze, Bjoern Golbabaee, Mohammad |
| author_facet | Mayo, Perla Pirkl, Carolin M. Achim, Alin Menze, Bjoern Golbabaee, Mohammad |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_23311 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics informed guided diffusion for accelerated multi-parametric MRI reconstruction Mayo, Perla Pirkl, Carolin M. Achim, Alin Menze, Bjoern Golbabaee, Mohammad Image and Video Processing Machine Learning Medical Physics 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. |
| title | Physics informed guided diffusion for accelerated multi-parametric MRI reconstruction |
| topic | Image and Video Processing Machine Learning Medical Physics |
| url | https://arxiv.org/abs/2506.23311 |