Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:3029-3034. doi: 10.1109/EMBC48229.2022.9871003.
Magnetic Resonance (MR) Fingerprinting is an emerging transient-state imaging paradigm, which enables the quantization of multiple MR tissue parameters in a single experiment. Balanced steady-state free precession (bSSFP)-based MR Fingerprinting has excellent signal-to-noise characteristics and also allows for acquiring both tissue parameter maps and field inhomogeneity maps. However, field inhomogeneity often results in complex magnetization evolutions in bSSFP-based MR Fingerprinting, which creates significant challenges in image reconstruction. In this paper, we introduce a new method to address the image reconstruction problem. The proposed method incorporates a low-dimensional nonlinear manifold learned from an ensemble of magnetization evolutions using a deep autoencoder. It provides much better representation power than a low-dimensional linear subspace in capturing complex magnetization evolutions. We formulate the image reconstruction problem with this nonlinear model and solve the resulting optimization problem using an algorithm based on variable splitting and the alternating direction method of multipliers. We evaluate the performance of the proposed method using numerical experiments and demonstrate that it significantly outperforms the state-of-art method using a linear subspace model.
磁共振（MR）指纹成像技术是一种新兴的瞬态成像范例，可在单次实验中对多个 MR 组织参数进行量化。基于平衡稳态自由进动（bSSFP）的 MR 指纹成像技术具有出色的信噪比特性，同时还可以获取组织参数图和磁场不均匀图。然而，磁场不均匀通常会导致 bSSFP 基 MR 指纹成像中的复杂磁化演变，这给图像重建带来了重大挑战。在本文中，我们介绍了一种解决图像重建问题的新方法。所提出的方法结合了从使用深度自动编码器的磁化演变的集合中学习到的低维非线性流形。与捕获复杂磁化演变的低维线性子空间相比，它提供了更好的表示能力。我们使用此非线性模型来构建图像重建问题，并使用基于变量分裂和交替方向乘子法的算法来解决由此产生的优化问题。我们使用数值实验评估了所提出方法的性能，并证明它在使用线性子空间模型方面明显优于最先进的方法。
