基於深度學習的磁振造影超解析度技術：綜述性研究

摘要

高分辨率磁共振成像在诸多临床与科研应用中具有关键作用，但实现该技术仍面临成本高昂、受技术权衡与实验条件限制等问题。超分辨率技术作为一种前景广阔的计算方法，可通过从更经济的低分辨率扫描数据生成高分辨率图像来突破这些限制，有望在不增加硬件成本的前提下提升诊断准确性与效率。本综述系统梳理了磁共振超分辨率技术的最新进展，重点关注深度学习方法，从计算机视觉、计算成像、逆问题及磁共振物理等多维视角剖析基于深度学习的磁共振超分辨率技术，涵盖理论基础、架构设计、学习策略、基准数据集与性能指标。我们提出系统性分类法对现有方法进行归类，并对适用于磁共振的经典与新兴超分辨率技术开展深度研究，同时考量临床与科研场景中的特殊挑战。文中还指明了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、工具及教程合集（详见GitHub项目：https://github.com/mkhateri/Awesome-MRI-Super-Resolution）。

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.

基於深度學習的磁振造影超解析度技術：綜述性研究

MRI Super-Resolution with Deep Learning: A Comprehensive Survey

摘要

Support