Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review

IF 8.9 1区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

IEEE Transactions on Medical Imaging Pub Date : 2023-05-11 DOI:10.48550/arXiv.2305.06739

Veronika Spieker, H. Eichhorn, K. Hammernik, D. Rueckert, C. Preibisch, D. Karampinos, J. Schnabel

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引用次数: 2

Abstract

Motion represents one of the major challenges in magnetic resonance imaging (MRI). Since the MR signal is acquired in frequency space, any motion of the imaged object leads to complex artefacts in the reconstructed image in addition to other MR imaging artefacts. Deep learning has been frequently proposed for motion correction at several stages of the reconstruction process. The wide range of MR acquisition sequences, anatomies and pathologies of interest, and motion patterns (rigid vs. deformable and random vs. regular) makes a comprehensive solution unlikely. To facilitate the transfer of ideas between different applications, this review provides a detailed overview of proposed methods for learning-based motion correction in MRI together with their common challenges and potentials. This review identifies differences and synergies in underlying data usage, architectures, training and evaluation strategies. We critically discuss general trends and outline future directions, with the aim to enhance interaction between different application areas and research fields.

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深度学习在MRI回顾性运动矫正中的应用综述

运动是磁共振成像（MRI）的主要挑战之一。由于MR信号是在频率空间中获取的，因此除了其他MR成像伪影之外，成像对象的任何运动都会导致重建图像中的复杂伪影。深度学习经常被提出用于重建过程的几个阶段的运动校正。广泛的MR采集序列、感兴趣的解剖结构和病理学以及运动模式（刚性与可变形、随机与规则）使综合解决方案变得不太可能。为了促进不同应用之间的思想交流，这篇综述详细概述了MRI中基于学习的运动校正方法，以及它们的常见挑战和潜力。这篇综述确定了基础数据使用、架构、培训和评估策略方面的差异和协同作用。我们批判性地讨论了总体趋势并概述了未来的方向，目的是加强不同应用领域和研究领域之间的互动。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Medical Imaging 医学-成像科学与照相技术

CiteScore

21.80

自引率

5.70%

发文量

637

审稿时长

5.6 months

期刊介绍： The IEEE Transactions on Medical Imaging (T-MI) is a journal that welcomes the submission of manuscripts focusing on various aspects of medical imaging. The journal encourages the exploration of body structure, morphology, and function through different imaging techniques, including ultrasound, X-rays, magnetic resonance, radionuclides, microwaves, and optical methods. It also promotes contributions related to cell and molecular imaging, as well as all forms of microscopy. T-MI publishes original research papers that cover a wide range of topics, including but not limited to novel acquisition techniques, medical image processing and analysis, visualization and performance, pattern recognition, machine learning, and other related methods. The journal particularly encourages highly technical studies that offer new perspectives. By emphasizing the unification of medicine, biology, and imaging, T-MI seeks to bridge the gap between instrumentation, hardware, software, mathematics, physics, biology, and medicine by introducing new analysis methods. While the journal welcomes strong application papers that describe novel methods, it directs papers that focus solely on important applications using medically adopted or well-established methods without significant innovation in methodology to other journals. T-MI is indexed in Pubmed® and Medline®, which are products of the United States National Library of Medicine.