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MPFlow: Multi-modal Posterior-Guided Flow Matching for Zero-Shot MRI Reconstruction

Seunghoi Kim, Chen Jin, Henry F. J. Tregidgo, Matteo Figini, Daniel C. Alexander

TL;DR

MPFlow is proposed, 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.

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 (PAMRI), 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.

MPFlow: Multi-modal Posterior-Guided Flow Matching for Zero-Shot MRI Reconstruction

TL;DR

MPFlow is proposed, 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.

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 (PAMRI), 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.
Paper Structure (16 sections, 9 equations, 2 figures, 3 tables)

This paper contains 16 sections, 9 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Method
  3. Problem Formulation
  4. Hypothesis and Justification
  5. Rectified Flow
  6. Patch-level Multi-modal MR Image Pretraining (PAMRI)
  7. Multi-modal Posterior-Guided Flow Matching
  8. Initial noise optimization
  9. Experiments
  10. Experimental Setup
  11. Datasets
  12. Implementation Details
  13. Baselines & Metrics
  14. Main Results
  15. Further Analysis
  16. ...and 1 more sections

Figures (2)

  • Figure 1: Schematic diagram of the proposed framework. (a) PAMRI: cross-modal feature alignment by learning a shared representation space across different MRI contrasts, (b) MPFlow: flow-matching prior is guided by both data consistency and PAMRI, to reduce intrinsic and extrinsic hallucinations.
  • Figure 2: Visual comparisons on HCP (top), BraTS (bottom). Highlighted red boxes show MPFlow clearly reduces hallucinations compared to the baselines.