Tapper Sofie, Mikkelsen Mark, Dewey Blake E, Zöllner Helge J, Hui Steve C N, Oeltzschner Georg, Edden Richard A E
Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA.
Magn Reson Med. 2021 Apr;85(4):1755-1765. doi: 10.1002/mrm.28525. Epub 2020 Nov 18.
To investigate whether a deep learning-based (DL) approach can be used for frequency-and-phase correction (FPC) of MEGA-edited MRS data.
Two neural networks (1 for frequency, 1 for phase) consisting of fully connected layers were trained and validated using simulated MEGA-edited MRS data. This DL-FPC was subsequently tested and compared to a conventional approach (spectral registration [SR]) and to a model-based SR implementation (mSR) using in vivo MEGA-edited MRS datasets. Additional artificial offsets were added to these datasets to further investigate performance.
The validation showed that DL-based FPC was capable of correcting within 0.03 Hz of frequency and 0.4°of phase offset for unseen simulated data. DL-based FPC performed similarly to SR for the unmanipulated in vivo test datasets. When additional offsets were added to these datasets, the networks still performed well. However, although SR accurately corrected for smaller offsets, it often failed for larger offsets. The mSR algorithm performed well for larger offsets, which was because the model was generated from the in vivo datasets. In addition, the computation times were much shorter using DL-based FPC or mSR compared to SR for heavily distorted spectra.
These results represent a proof of principle for the use of DL for preprocessing MRS data.
研究基于深度学习（DL）的方法是否可用于Mega编辑的磁共振波谱（MRS）数据的频率和相位校正（FPC）。
使用模拟的Mega编辑的MRS数据训练并验证了两个由全连接层组成的神经网络（一个用于频率，一个用于相位）。随后，使用体内Mega编辑的MRS数据集对这种DL-FPC进行了测试，并与传统方法（频谱配准[SR]）和基于模型的SR实现（mSR）进行了比较。向这些数据集中添加了额外的人工偏移，以进一步研究性能。
验证表明，基于DL的FPC能够对未见过的模拟数据进行频率在0.03Hz以内、相位偏移在0.4°以内的校正。对于未处理的体内测试数据集，基于DL的FPC的性能与SR相似。当向这些数据集中添加额外的偏移时，网络仍然表现良好。然而，尽管SR能准确校正较小的偏移，但对于较大的偏移往往会失败。mSR算法在处理较大偏移时表现良好，这是因为该模型是从体内数据集生成的。此外，对于严重失真的光谱，与SR相比，使用基于DL的FPC或mSR时计算时间要短得多。
这些结果证明了使用DL对MRS数据进行预处理的原理。
