Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks
Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks
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DOI：
10.1109/tmi.2019.2911203
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发表时间：
2019-12-01
影响因子：
10.6
通讯作者：
Shen, Dinggang
中科院分区：
文献类型：
--
作者：
Hong, Yoonmi;Kim, Jaeil;Shen, Dinggang
由于受试者脱落和扫描失败，缺失数据是纵向研究中的常见问题。我们提出了一个基于图的卷积神经网络来预测丢失的扩散MRI数据。特别地，我们考虑在空间域和扩散波矢量域中的采样点之间的关系来构造图。然后，我们使用图卷积网络来学习从可用数据到缺失数据的非线性映射。我们的方法利用具有对抗学习的多尺度残差架构进行预测，具有更高的准确性和感知质量。实验结果表明，我们的方法是准确的和强大的婴儿脑扩散MRI数据的纵向预测。
Missing data is a common problem in longitudinal studies due to subject dropouts and failed scans. We present a graph-based convolutional neural network to predict missing diffusion MRI data. In particular, we consider the relationships between sampling points in the spatial domain and the diffusion wave-vector domain to construct a graph. We then use a graph convolutional network to learn the non-linear mapping from available data to missing data. Our method harnesses a multi-scale residual architecture with adversarial learning for prediction with greater accuracy and perceptual quality. Experimental results show that our method is accurate and robust in the longitudinal prediction of infant brain diffusion MRI data.