论文标题
仅用于磁化转移对比量化的唯一训练MR指纹识别
Only-Train-Once MR Fingerprinting for Magnetization Transfer Contrast Quantification
论文作者
论文摘要
磁化转移对比度磁共振指纹（MTC-MRF）是一种新型的定量成像技术，同时测量了半固体大分子和游离散装水的几个组织参数。在这项研究中，我们提出了一个唯一的训练MR指纹（OTOM）框架，该框架估算MR指纹的自由水和MTC组织参数，无论MRF时间表如何，从而避免了时间耗时的过程，例如根据每个MRF计划生成训练数据集和网络培训。复发性神经网络旨在应对MRF计划的两种类型的变体：1）各种长度和2）各种模式。关于数字幻象和体内数据的实验表明，我们的方法可以通过多个MRF时间表来实现水和MTC参数的准确定量。此外，提出的方法与常规的深度学习和拟合方法非常吻合。灵活的OTOM框架可能是各种MRF方案的有效组织定量工具。
Magnetization transfer contrast magnetic resonance fingerprinting (MTC-MRF) is a novel quantitative imaging technique that simultaneously measures several tissue parameters of semisolid macromolecule and free bulk water. In this study, we propose an Only-Train-Once MR fingerprinting (OTOM) framework that estimates the free bulk water and MTC tissue parameters from MR fingerprints regardless of MRF schedule, thereby avoiding time-consuming process such as generation of training dataset and network training according to each MRF schedule. A recurrent neural network is designed to cope with two types of variants of MRF schedules: 1) various lengths and 2) various patterns. Experiments on digital phantoms and in vivo data demonstrate that our approach can achieve accurate quantification for the water and MTC parameters with multiple MRF schedules. Moreover, the proposed method is in excellent agreement with the conventional deep learning and fitting methods. The flexible OTOM framework could be an efficient tissue quantification tool for various MRF protocols.
