Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks.
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ABSTRACT:
SUBMITTER: Hong Y
PROVIDER: S-EPMC6935161 | biostudies-literature | 2019 Dec
REPOSITORIES: biostudies-literature
Publications
Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks.
Hong Yoonmi Y Kim Jaeil J Chen Geng G Lin Weili W Yap Pew-Thian PT Shen Dinggang D
IEEE transactions on medical imaging 20190415 12
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 arc ...[more]
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