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Real-time Video Projection in an MRI for Characterization of Neural Correlates Associated with Mirror Therapy for Phantom Limb Pain

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On the Utility of Foundation Models for Fast MRI: Vision-Language-Guided Image Reconstruction.

Ruimin Feng^1,2, Xingxin He^1,2, Ronald Mercer^2,3

¹Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.

Magnetic Resonance in Medicine

|April 8, 2026

Summary

This summary is machine-generated.

Vision-language models improve undersampled MRI reconstruction by adding semantic context. This approach enhances image quality and anatomical detail beyond traditional methods.

Keywords:

contrastive loss fast MRI semantic prior vision‐language foundation model

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Area of Science:

Medical Imaging
Artificial Intelligence

Background:

Undersampled Magnetic Resonance Imaging (MRI) suffers from reduced image quality and detail.
Conventional MRI reconstruction methods rely on limited prior information, often insufficient for complex anatomical structures.

Purpose of the Study:

To investigate the efficacy of vision-language foundation models in enhancing undersampled MRI reconstruction.
To explore the use of high-level contextual information from foundation models to improve MRI reconstruction quality.

Main Methods:

A novel semantic distribution-guided reconstruction framework was proposed.
This framework utilizes a pre-trained vision-language foundation model to extract high-level semantic features from reconstructed images and auxiliary data.
A contrastive objective function aligns reconstructed image representations with target semantic distributions, incorporating perceptual cues.

Main Results:

Experiments on knee and brain MRI datasets showed that semantic priors improved anatomical structure preservation and perceptual quality.
Image-only and image-language priors led to superior results compared to conventional regularization, evidenced by lower LPIPS and higher Tenengrad scores.
The contrastive objective effectively guided feature reconstruction towards desired semantic distributions while maintaining data fidelity.

Conclusions:

Vision-language foundation models offer a powerful approach to enhance undersampled MRI reconstruction.
Semantic-space optimization using these models can significantly improve image quality and diagnostic utility.