MPFlow: Multi-modal Posterior-Guided Flow Matching for Zero-Shot MRI Reconstruction
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arXiv:2603.03710v1 Announce Type: cross
Abstract: Zero-shot MRI reconstruction relies on generative priors, but single-modality unconditional priors produce hallucinations under severe ill-posedness. In many clinical workflows, complementary MRI acquisitions (e.g. high-quality structural scans) are routinely available, yet existing reconstruction methods lack mechanisms to leverage this additional information. We propose MPFlow, a zero-shot multi-modal reconstruction framework built on rectifie
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--> Computer Science > Computer Vision and Pattern Recognition arXiv:2603.03710 [Submitted on 4 Mar 2026] Title: MPFlow: Multi-modal Posterior-Guided Flow Matching for Zero-Shot MRI Reconstruction Authors: Seunghoi Kim , Chen Jin , Henry F. J. Tregidgo , Matteo Figini , Daniel C. Alexander View a PDF of the paper titled MPFlow: Multi-modal Posterior-Guided Flow Matching for Zero-Shot MRI Reconstruction, by Seunghoi Kim and Chen Jin and Henry F. J. Tregidgo and Matteo Figini and Daniel C. Alexander View PDF HTML Abstract: Zero-shot MRI reconstruction relies on generative priors, but single-modality unconditional priors produce hallucinations under severe ill-posedness. In many clinical workflows, complementary MRI acquisitions (e.g. high-quality structural scans) are routinely available, yet existing reconstruction methods lack mechanisms to leverage this additional information. We propose MPFlow, a zero-shot multi-modal reconstruction framework built on rectified flow that incorporates auxiliary MRI modalities at inference time without retraining the generative prior to improve anatomical fidelity. Cross-modal guidance is enabled by our proposed self-supervised pretraining strategy, Patch-level Multi-modal MR Image Pretraining , which learns shared representations across modalities. Sampling is jointly guided by data consistency and cross-modal feature alignment using pre-trained PAMRI, systematically suppressing intrinsic and extrinsic hallucinations. Extensive experiments on HCP and BraTS show that MPFlow matches diffusion baselines on image quality using only 20% of sampling steps while reducing tumor hallucinations by more than 15% (segmentation dice score). This demonstrates that cross-modal guidance enables more reliable and efficient zero-shot MRI reconstruction. Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2603.03710 [cs.CV] (or arXiv:2603.03710v1 [cs.CV] for this version) https://doi.org/10.48550...
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