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中国工业与应用数学学会会刊
主管：中华人民共和国教育部
主办：西安交通大学
ISSN 1005-3085  CN 61-1269/O1
工程数学学报

工程数学学报 ›› 2024, Vol. 41 ›› Issue (3): 397-409.doi: 10.3969/j.issn.1005-3085.2024.03.001

• •    下一篇

快速磁共振成像的采样优化综述

李  星,  杨  燕,  靖稳峰   

  1. 西安交通大学数学与统计学院大数据算法与分析技术国家工程实验室，西安 710049
  • 收稿日期:2021-11-25 接受日期:2022-07-04 出版日期:2024-06-15 发布日期:2024-08-15
  • 通讯作者: 靖稳峰 E-mail: wfjing@xjtu.edu.cn
  • 基金资助:
    国家重点研发计划 (2022YFA1004201)；国家自然科学基金 (11631013)；国家自然科学基金–广东联合基金 (U21A6005HZ).

Sampling Patterns in Accelerating Magnetic Resonance Imaging: a Survey

LI Xing,  YANG Yan,  JING Wenfeng   

  1. National Engineering Laboratory for Big Data Analytics, School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049
  • Received:2021-11-25 Accepted:2022-07-04 Online:2024-06-15 Published:2024-08-15
  • Contact: W. Jing. E-mail address: wfjing@xjtu.edu.cn
  • Supported by:
    The National Key Research and Development Program (2022YFA1004201); the National Natural Science Foundation of China (11631013); the National Natural Science Foundation of China--Guangdong Joint Fund (U21A6005HZ).

摘要：

快速磁共振成像一直都是磁共振成像 (Magnetic Resonance Imaging, MRI) 的核心研究内容，通过$k$空间欠采样数据重建或增加多个线圈并行成像 (并行MRI技术) 能够有效地提高扫描速度，降低核磁共振检查的扫描时间，已广泛应用于临床医学。近年来，随着深度学习技术的发展，将深度学习方法应用到磁共振快速成像取得了突破性的进展，基于深度学习的磁共振快速成像以其更快的扫描、更快的成像优势成为目前磁共振成像领域的研究热点，在欠采样倍数较高的情况下仍然能重建出伪影较低的高质量MRI图像。基于此，首先简要回顾了传统的快速MRI采样方法，之后对基于深度学习的快速磁共振成像欠采样与重建联合优化框架进行综述，并展示了相关框架的性能比较，最后对快速磁共振成像采样的发展趋势进行了展望。

关键词: 快速磁共振成像, 深度学习, 医学影像, 图像重建, 欠采样模式

Abstract:

Accelerating MRI has been widely used in clinical medicine by reconstruction after undersampling in $k$-space and parallel MRI technology can effectively reduce the scanning time in MRI examination. Driven by deep learning technology which involved in accelerating MRI has made a breakthrough. Accelerating MRI based on deep learning has become the research hotspot in the field of MRI with its faster scanning and imaging. The high quality of MRI images with less artifacts can be reconstructed even with lower sample ratio. In this paper, we first briefly reviews the traditional accelerating MRI sampling methods and then introduce the joint optimization framework of under-sampling and reconstruction based on deep learning in accelerating MRI by comparing the performance of relevant frameworks. Finally, we discuss the development trend of accelerating MRI sampling.

Key words: accelerating MRI, deep learning, medical imaging, image reconstruction, undersampled pattern

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