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ARXIV:2603.03710 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.03710MEDICAL AISUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Leverage MPFlow for zero-shot multi-modal MRI reconstruction with enhanced anatomical fidelity using cross-modal guidance.
Opportunity summary
Pain Leverage MPFlow for zero-shot multi-modal MRI reconstruction with enhanced anatomical fidelity using cross-modal guidance.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Leverage MPFlow for zero-shot multi-modal MRI reconstruction with enhanced anatomical fidelity using cross-modal guidance. In many clinical workflows, complementary MRI acquisitions (e.g.
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.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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…
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
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Leverage MPFlow for zero-shot multi-modal MRI reconstruction with enhanced anatomical fidelity using cross-modal guidance.
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Paper Pack
10.48550/arXiv.2603.03710Leverage MPFlow for zero-shot multi-modal MRI reconstruction with enhanced anatomical fidelity using cross-modal guidance.
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.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
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Dimensions overall score 7.0
PROBLEM
Leverage MPFlow for zero-shot multi-modal MRI reconstruction with enhanced anatomical fidelity using cross-modal guidance. In many clinical workflows, complementary MRI acquisitions (e.g.
METHOD
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.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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...
WHY NOW
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Leverage MPFlow for zero-shot multi-modal MRI reconstruction with enhanced anatomical fidelity using cross-modal guidance. In many clinical workflows, complementary MRI acquisitions (e.g.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Leverage MPFlow for zero-shot multi-modal MRI reconstruction with enhanced anatomical fidelity using cross-modal guidance.
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Medical AI
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7.0/10 public viability
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proof status
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