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.

翻译：磁共振指纹(MRF)能够快速和多参数的MR成像。尽管快速获取,但基于字典匹配的最新MRF重建缓慢且缺乏可缩放性。为了克服这些局限性,最近提出了从指纹中估计MR参数的神经网络方法。在这里,我们重新研究NNMMF重建,以便通过利用可逆的神经网络(INNs),共同学习从MR参数到指纹的前瞻性进程,并从指纹到MR参数的后向进程。作为一个证据概念,我们进行了各种实验,展示了学习前向进程(即布洛奇模拟)的好处,以改进MRR参数估计。当MR参数估计因MR物理限制而难以估计时,这种好处尤其突出。因此,INS可能是目前仅用于MRF重建的后向型NW的替代方法。