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arXiv · 2503.07097

A Comprehensive Survey on Magnetic Resonance Image Reconstruction

Xiaoyan Kui·Zijie Fan·Zexin Ji·Qinsong Li·Chengtao Liu·Weixin Si·Beiji Zou
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Abstract

Magnetic resonance imaging (MRI) reconstruction is a fundamental task aimed at recovering high-quality images from undersampled or low-quality MRI data. This process enhances diagnostic accuracy and optimizes clinical applications. In recent years, deep learning-based MRI reconstruction has made significant progress. Advancements include single-modality feature extraction using different network architectures, the integration of multimodal information, and the adoption of unsupervised or semi-supervised learning strategies. However, despite extensive research, MRI reconstruction remains a challenging problem that has yet to be fully resolved. This survey provides a systematic review of MRI reconstruction methods, covering key aspects such as data acquisition and preprocessing, publicly available datasets, single and multi-modal reconstruction models, training strategies, and evaluation metrics based on image reconstruction and downstream tasks. Additionally, we analyze the major challenges in this field and explore potential future directions.

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Xiaoyan Kui, Zijie Fan, Zexin Ji, Qinsong Li, Chengtao Liu, Weixin Si, Beiji Zou. 2025-03-10. A Comprehensive Survey on Magnetic Resonance Image Reconstruction. https://arxiv.org/abs/2503.07097

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