Frequency Error-Guided Under-sampling Optimization for Multi-Contrast MRI Reconstruction

• URL: http://arxiv.org/abs/2601.09316v1
• Date: Wed, 14 Jan 2026 09:40:34 GMT
• Title: Frequency Error-Guided Under-sampling Optimization for Multi-Contrast MRI Reconstruction
• Authors: Xinming Fang, Chaoyan Huang, Juncheng Li, Jun Wang, Jun Shi, Guixu Zhang,
• Abstract summary: Multi-contrast MRI reconstruction has emerged as a promising direction by leveraging complementary information from fully-sampled reference scans.<n>Existing approaches suffer from three major limitations: (1) superficial reference fusion strategies, (2) insufficient utilization of the complementary information provided by the reference contrast, and (3) fixed under-sampling patterns.<n>We propose an efficient and interpretable frequency error-guided reconstruction framework to tackle these issues.
• Score: 24.246450246745905
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Magnetic resonance imaging (MRI) plays a vital role in clinical diagnostics, yet it remains hindered by long acquisition times and motion artifacts. Multi-contrast MRI reconstruction has emerged as a promising direction by leveraging complementary information from fully-sampled reference scans. However, existing approaches suffer from three major limitations: (1) superficial reference fusion strategies, such as simple concatenation, (2) insufficient utilization of the complementary information provided by the reference contrast, and (3) fixed under-sampling patterns. We propose an efficient and interpretable frequency error-guided reconstruction framework to tackle these issues. We first employ a conditional diffusion model to learn a Frequency Error Prior (FEP), which is then incorporated into a unified framework for jointly optimizing both the under-sampling pattern and the reconstruction network. The proposed reconstruction model employs a model-driven deep unfolding framework that jointly exploits frequency- and image-domain information. In addition, a spatial alignment module and a reference feature decomposition strategy are incorporated to improve reconstruction quality and bridge model-based optimization with data-driven learning for improved physical interpretability. Comprehensive validation across multiple imaging modalities, acceleration rates (4-30x), and sampling schemes demonstrates consistent superiority over state-of-the-art methods in both quantitative metrics and visual quality. All codes are available at https://github.com/fangxinming/JUF-MRI.

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