系统梳理深度学习在磁共振成像重建中的方法与挑战。
A Comprehensive Survey on Magnetic Resonance Image Reconstruction
- 按数据采集、预处理、模型架构等维度分类梳理重建方法
- 涵盖单模态与多模态模型，及无监督/半监督训练策略
- 适合医学影像研究者和深度学习应用开发者参考
磁共振成像（MRI）重建旨在从欠采样或低质量数据中恢复高质量图像，提升诊断准确性和临床效率。近年来，基于深度学习的MRI重建取得显著进展，包括不同网络结构的单模态特征提取、多模态信息融合以及无监督或半监督学习策略的应用。然而，该问题仍未完全解决。本综述系统回顾了MRI重建的关键方面：数据采集与预处理、公开数据集、单模态与多模态重建模型、训练策略及基于图像重建和下游任务的评估指标。同时分析领域主要挑战并探讨未来方向。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。