UniField: A Unified Field-Aware MRI Enhancement Framework

2026-03-10

AI Summary

This work addresses the limited generalization of existing MRI field-strength enhancement methods and their failure to effectively exploit degradation patterns shared across field strengths. To overcome these limitations, we propose a unified multi-task learning framework that integrates multi-contrast MRI enhancement by leveraging shared degradation features to improve representational capacity. Key innovations include 3D foundation model–based volumetric data modeling and a physics-informed, field-strength-aware spectral refinement module (FASRM). We also release the largest paired multi-field-strength MRI dataset to date. Experimental results demonstrate that our approach achieves an average improvement of 1.81 dB in PSNR and 9.47% in SSIM, significantly outperforming current state-of-the-art methods.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Multi-modal Vision
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Magnetic Resonance Imaging (MRI) field-strength enhancement holds immense value for both clinical diagnostics and advanced research. However, existing methods typically focus on isolated enhancement tasks, such as specific 64mT-to-3T or 3T-to-7T transitions using limited subject cohorts, thereby failing to exploit the shared degradation patterns inherent across different field strengths and severely restricting model generalization. To address this challenge, we propose \methodname, a unified framework integrating multiple modalities and enhancement tasks to mutually promote representation learning by exploiting these shared degradation characteristics. Specifically, our main contributions are threefold. Firstly, to overcome MRI data scarcity and capture continuous anatomical structures, \methodname departs from conventional methods that treat 3D MRI volumes as independent 2D slices. Instead, we directly exploit comprehensive 3D volumetric information by leveraging pre-trained 3D foundation models, thereby embedding generalized and robust structural representations to significantly boost enhancement performance. In addition, to mitigate the spectral bias of mainstream flow-matching models that often over-smooth high-frequency details, we explicitly incorporate the physical mechanisms of magnetic fields to introduce a Field-Aware Spectral Rectification Mechanism (FASRM), tailoring customized spectral corrections to distinct field strengths. Finally, to resolve the fundamental data bottleneck, we organize and publicly release a comprehensive paired multi-field MRI dataset, which is an order of magnitude larger than existing datasets. Extensive experiments demonstrate our method's superiority over state-of-the-art approaches, achieving an average improvement of approximately 1.81 dB in PSNR and 9.47\% in SSIM. Code will be released upon acceptance.

Problem

• MRI enhancement
• field-strength generalization
• degradation patterns
• multi-field MRI
• model generalization

Innovation

• Unified MRI Enhancement
• 3D Foundation Model
• Field-Aware Spectral Rectification
• Multi-Field MRI Dataset
• Flow-Matching Bias Mitigation