Magnetic resonance fingerprinting (MRF) is a magnetic resonance imaging (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 the 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 the 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 (cMRF).

中文翻译：

---

用于快速磁共振指纹组织特性量化的机器学习

磁共振指纹图谱 (MRF) 是一种基于磁共振成像 (MRI) 的方法，可以通过一次快速采集同时提供多种组织特性的定量图。组织特性图是通过将扫描仪收集的复杂信号演变与使用 Bloch 方程模拟得出的信号字典相匹配来生成的。但是，在某些情况下，字典生成和信号匹配的过程可能非常耗时，从而降低了该技术的实用性。最近，几个小组提出使用机器学习来加速从 MRF 数据中提取定量图。本文将概述当前结合 MRF 和机器学习的研究，