Hamilton Jesse I, Seiberlich Nicole
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106 USA, and the Department of Radiology, University of Michigan, Ann Arbor, MI 48109.
Department of Biomedical Engineering and the Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106 USA, the Department of Radiology and Cardiology, University Hospitals, Cleveland, OH 44106 USA, and the Department of Radiology, University of Michigan, Ann Arbor, MI 48109.
Proc IEEE Inst Electr Electron Eng. 2020 Jan;108(1):69-85. doi: 10.1109/JPROC.2019.2936998. Epub 2019 Sep 11.
Magnetic Resonance Fingerprinting (MRF) is an MRI-based method that can provide quantitative maps of multiple tissue properties simultaneously from a single rapid acquisition. Tissue property maps are generated by matching the complex signal evolutions collected at the scanner to a dictionary of signals derived using Bloch equation simulations. However, in some circumstances, the process of dictionary generation and signal matching can be time-consuming, reducing the utility of this technique. Recently, several groups have proposed using machine learning to accelerate the extraction of quantitative maps from MRF data. This article will provide an overview of current research that combines MRF and machine learning, as well as present original research demonstrating how machine learning can speed up dictionary generation for cardiac MRF.
磁共振指纹识别（MRF）是一种基于磁共振成像（MRI）的方法，它能够通过单次快速采集同时提供多种组织特性的定量图谱。组织特性图谱是通过将在扫描仪处采集到的复杂信号演变与使用布洛赫方程模拟得出的信号字典进行匹配而生成的。然而，在某些情况下，字典生成和信号匹配的过程可能会很耗时，从而降低了这项技术的实用性。最近，几个研究小组提出使用机器学习来加速从MRF数据中提取定量图谱。本文将概述当前将MRF与机器学习相结合的研究，同时展示原创研究，说明机器学习如何能够加快心脏MRF的字典生成。
