Accelerated Simultaneous Multi-Slice MRI using Subject-Specific Convolutional Neural Networks
Accelerated Simultaneous Multi-Slice MRI using Subject-Specific Convolutional Neural Networks
复制标题
使用特定主题的卷积神经网络加速同步多层 MRI
DOI：
10.1109/acssc.2018.8645313
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Akcakaya, Mehmet
中科院分区：
文献类型：
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作者：
Zhang, Chi;Moeller, Steen;Weingartner, Sebastian;Ugurbil, Kamil;Akcakaya, Mehmet
同时多层或多波段（SMS/MB）成像可以加速磁共振成像（MRI）的覆盖范围。多个切片同时被激发和获取，并利用接收线圈阵列中的冗余重构，类似于并行成像。SMS/MB重建目前采用线性重建技术。近年来，一种用于并行成像的非线性重建方法——鲁棒人工神经网络k空间插值（RAKI）被提出并证明是对线性方法的改进。该方法使用卷积神经网络（CNN），该网络仅在特定主题的校准数据上训练。在本研究中，我们试图将RAKI扩展到SMS/MB成像重建。对SMS/MB成像前获取的校准数据进行CNN训练，训练方式与现有的线性方法一致。这些cnn被用来重建功能MRI （fMRI）数据的时间序列。通过对参数空间的广泛搜索，对CNN网络参数进行优化。有了这些最优参数，RAKI与常用的线性重建算法相比，大大提高了图像质量，特别是在高加速率下。
Simultaneous multi-slice or multi-band (SMS/MB) imaging allows accelerated coverage in magnetic resonance imaging (MRI). Multiple slices are excited and acquired at the same time, and reconstructed using the redundancies in receiver coil arrays, similar to parallel imaging. SMS/MB reconstruction is currently performed with linear reconstruction techniques. Recently, a nonlinear reconstruction method for parallel imaging, Robust Artificial-neural-networks for k-space Interpolation (RAKI) was proposed and shown to improve upon linear methods. This method uses convolutional neural networks (CNN) trained solely on subject-specific calibration data. In this study, we sought to extend RAKI to SMS/MB imaging reconstruction. CNN training was performed on calibration data acquired prior to SMS/MB imaging, in a manner consistent with the existing linear methods. These CNNs were used to reconstruct a time series of functional MRI (fMRI) data. CNN network parameters were optimized using an extensive search of the parameter space. With these optimal parameters, RAKI substantially improves image quality compared to a commonly used linear reconstruction algorithm, especially for high acceleration rates.