基于深度学习的MRI超分辨率技术：全面综述

摘要

高分辨率磁共振成像在众多临床与科研应用中具有关键作用。然而受限于技术权衡与实验条件，实现高分辨率成像仍成本高昂。超分辨率技术作为一种前景广阔的计算方法，通过从更易获取的低分辨率扫描数据生成高分辨率图像，有望在不增加硬件负担的前提下提升诊断精度与效率。本文系统综述了磁共振超分辨率技术的最新进展，重点探讨深度学习方案，从计算机视觉、计算成像、逆问题及磁共振物理等多维度剖析基于深度学习的磁共振超分辨率方法，涵盖理论基础、架构设计、学习策略、基准数据集与性能指标。我们提出系统分类法对现有方法进行归类，并结合临床与研究场景中的特殊挑战，对成熟及新兴的超分辨率技术展开深入研究。同时指出该领域亟待解决的关键问题与发展方向，并汇总了开源资源、工具及教程集（GitHub: https://github.com/mkhateri/Awesome-MRI-Super-Resolution）。 IEEE关键词：磁共振成像，超分辨率，深度学习，计算成像，逆问题，综述。

English

High-resolution (HR) magnetic resonance imaging (MRI) is crucial for many clinical and research applications. However, achieving it remains costly and constrained by technical trade-offs and experimental limitations. Super-resolution (SR) presents a promising computational approach to overcome these challenges by generating HR images from more affordable low-resolution (LR) scans, potentially improving diagnostic accuracy and efficiency without requiring additional hardware. This survey reviews recent advances in MRI SR techniques, with a focus on deep learning (DL) approaches. It examines DL-based MRI SR methods from the perspectives of computer vision, computational imaging, inverse problems, and MR physics, covering theoretical foundations, architectural designs, learning strategies, benchmark datasets, and performance metrics. We propose a systematic taxonomy to categorize these methods and present an in-depth study of both established and emerging SR techniques applicable to MRI, considering unique challenges in clinical and research contexts. We also highlight open challenges and directions that the community needs to address. Additionally, we provide a collection of essential open-access resources, tools, and tutorials, available on our GitHub: https://github.com/mkhateri/Awesome-MRI-Super-Resolution. IEEE keywords: MRI, Super-Resolution, Deep Learning, Computational Imaging, Inverse Problem, Survey.

基于深度学习的MRI超分辨率技术：全面综述

MRI Super-Resolution with Deep Learning: A Comprehensive Survey

摘要

Support