波谱学杂志 >

2023 , Vol. 40 >Issue 2: 207 - 219

DOI: https://doi.org/10.11938/cjmr20223034

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磁共振指纹成像技术及临床应用的进展

  • 黄敏 ,
  • 李思怡 ,
  • 陈军波 ,
  • 周到
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  • 1.中南民族大学 生物医学工程学院，湖北 武汉 430074
    2.医学信息分析及肿瘤诊疗湖北省重点实验室，湖北 武汉 430074

收稿日期: 2022-11-14

  网络出版日期: 2023-03-02

基金资助

湖北省自然科学基金资助项目(2020CFB837);中央高校基本科研业务费专项资金资助项目(CZZ21006)

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Progress of Magnetic Resonance Fingerprinting Technology and Its Clinical Application

  • Min HUANG ,
  • Siyi LI ,
  • Junbo CHEN ,
  • Dao ZHOU
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  • 1. School of Biomedical Engineering, South-Central Minzu University, Wuhan 430074, China
    2. Hubei Key Laboratory of Medical Information Analysis and Tumor Diagnosis & Treatment, Wuhan 430074, China

Received date: 2022-11-14

  Online published: 2023-03-02

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摘要

磁共振指纹（magnetic resonance fingerprinting，MRF）是一种革新性的快速定量磁共振新技术，本文在成像技术和临床应用两个层面对MRF进行了综述. 在成像技术方面，主要从数据采集、字典建立，以及传统量化框架到深度学习量化框架的模式识别这3个步骤进行论述，分析存在的技术难点. 然后对MRF在人体重要部位的临床应用进行了总结，介绍了MRF技术在重复性和再现性方面的验证现状. 最后，本文分析了MRF走向临床存在的各种技术挑战及障碍，对MRF技术未来的发展方向进行了展望.

关键词： 磁共振指纹; 数据采集; 字典建立; 模式识别; 深度学习网络; 临床应用; 可重复性

本文引用格式

黄敏 , 李思怡 , 陈军波 , 周到 . 磁共振指纹成像技术及临床应用的进展[J]. 波谱学杂志, 2023 , 40(2) : 207 -219 . DOI: 10.11938/cjmr20223034

Abstract

Magnetic resonance fingerprinting (MRF) is a revolutionary new technique for rapid quantitative magnetic resonance imaging. We reviewed the imaging technology and clinical application of MRF in an all-round way. We focus on three technical aspects: data collection, dictionary generation, and pattern recognition from traditional quantitative framework to deep learning quantitative framework. We also analyzed the technical challenges and limitations of MRF. The clinical applications of MRF in various human body regions were summarized, and the current status of MRF technology verification in terms of repeatability and reproducibility was introduced. Finally, we discussed the potential barriers and opportunities for MRF to enter clinical application and envision the future development direction of MRF technology.

Key words： magnetic resonance fingerprinting; data acquisition; dictionary generation; pattern recognition; deep learning net; clinical application; repeatability

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