论文标题
通过可逆神经网络学习BLOCH模拟用于MR指纹的模拟
Learning Bloch Simulations for MR Fingerprinting by Invertible Neural Networks
论文作者
论文摘要
磁共振指纹（MRF）可实现快速和多参数MR成像。尽管获得了快速获取，但基于字典匹配的MRF的最新重建速度很慢，缺乏可扩展性。为了克服这些局限性，最近提出了估计指纹MR参数的神经网络（NN）方法。在这里，我们重新访问了基于NN的MRF重建，以共同学习从MR参数到指纹的远期过程，以及通过利用可逆神经网络（INNS）来利用可逆性神经网络（INNS），从指纹到MR参数的向后过程。作为概念验证，我们执行各种实验，显示学习远期过程的好处，即Bloch模拟，以改善MR参数估计。当MR参数估计因MR物理限制而难以进行时，尤其是突出的好处。因此，Inns可能是当前仅基于落后的NN的MRF重建的可行替代方法。
Magnetic resonance fingerprinting (MRF) enables fast and multiparametric MR imaging. Despite fast acquisition, the state-of-the-art reconstruction of MRF based on dictionary matching is slow and lacks scalability. To overcome these limitations, neural network (NN) approaches estimating MR parameters from fingerprints have been proposed recently. Here, we revisit NN-based MRF reconstruction to jointly learn the forward process from MR parameters to fingerprints and the backward process from fingerprints to MR parameters by leveraging invertible neural networks (INNs). As a proof-of-concept, we perform various experiments showing the benefit of learning the forward process, i.e., the Bloch simulations, for improved MR parameter estimation. The benefit especially accentuates when MR parameter estimation is difficult due to MR physical restrictions. Therefore, INNs might be a feasible alternative to the current solely backward-based NNs for MRF reconstruction.
