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.
@article{arxiv.2503.07097,
title = {A Comprehensive Survey on Magnetic Resonance Image Reconstruction},
author = {Xiaoyan Kui and Zijie Fan and Zexin Ji and Qinsong Li and Chengtao Liu and Weixin Si and Beiji Zou},
journal= {arXiv preprint arXiv:2503.07097},
year = {2025}
}