Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks

• Published in: IEEE transactions on medical imaging Vol. 38; no. 12; pp. 2717 - 2725
• Main Authors: Hong, Yoonmi, Kim, Jaeil, Chen, Geng, Lin, Weili, Yap, Pew-Thian, Shen, Dinggang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: adversarial learning; Artificial neural networks; Chebyshev approximation; Convolution; Correlation analysis; Diffusion; diffusion MRI; Diffusion waves; early brain development; Generators; Graph CNN; Harnesses; Laplace equations; longitudinal prediction; Longitudinal studies; Loss measurement; Magnetic resonance imaging; Mapping; Missing data; Multiscale analysis; Neural networks; Training
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2019.2911203

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.