Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic.
Department of Biomedical Engineering, Brno University of Technology, Brno, Czech Republic.
Magn Reson Med. 2023 Mar;89(3):1221-1236. doi: 10.1002/mrm.29498. Epub 2022 Nov 11.
A supervised deep learning (DL) approach for frequency and phase correction (FPC) of MRS data recently showed encouraging results, but obtaining transients with labels for supervised learning is challenging. This work investigates the feasibility and efficiency of unsupervised deep learning-based FPC.
Two novel deep learning-based FPC methods (deep learning-based Cr referencing and deep learning-based spectral registration), which use a priori physics domain knowledge, are presented. The proposed networks were trained, validated, and evaluated using simulated, phantom, and publicly accessible in vivo MEGA-edited MRS data. The performance of our proposed FPC methods was compared with other generally used FPC methods, in terms of precision and time efficiency. A new measure was proposed in this study to evaluate the FPC method performance. The ability of each of our methods to carry out FPC at varying SNR levels was evaluated. A Monte Carlo study was carried out to investigate the performance of our proposed methods.
The validation using low-SNR manipulated simulated data demonstrated that the proposed methods could perform FPC comparably with other methods. The evaluation showed that the deep learning-based spectral registration over a limited frequency range method achieved the highest performance in phantom data. The applicability of the proposed method for FPC of GABA-edited in vivo MRS data was demonstrated. Our proposed networks have the potential to reduce computation time significantly.
The proposed physics-informed deep neural networks trained in an unsupervised manner with complex data can offer efficient FPC of large MRS data in a shorter time.
最近，一种用于磁共振波谱（MRS）数据频率和相位校正（FPC）的监督深度学习（DL）方法取得了令人鼓舞的结果，但获得具有标签的瞬态数据进行监督学习具有挑战性。本研究旨在探讨基于无监督深度学习的 FPC 的可行性和效率。
提出了两种新的基于深度学习的 FPC 方法（基于深度学习的 Cr 参照和基于深度学习的谱注册），它们使用先验物理域知识。使用模拟、体模和公开可用的体内 MEGA 编辑 MRS 数据对所提出的网络进行训练、验证和评估。我们提出的 FPC 方法的性能与其他常用的 FPC 方法在精度和时间效率方面进行了比较。本研究提出了一种新的度量标准来评估 FPC 方法的性能。评估了每种方法在不同 SNR 水平下进行 FPC 的能力。进行了蒙特卡罗研究以研究所提出方法的性能。
使用低 SNR 处理的模拟数据进行验证表明，所提出的方法可以与其他方法相媲美地进行 FPC。评估表明，在有限频率范围内基于深度学习的谱注册方法在体模数据中表现出最高的性能。还证明了所提出的方法在 GABA 编辑的体内 MRS 数据的 FPC 中的适用性。所提出的网络具有显著减少计算时间的潜力。
用复杂数据进行无监督训练的基于物理信息的深度神经网络可以在更短的时间内高效地进行大型 MRS 数据的 FPC。
