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

Yoonmi Hong, Jaeil Kim, Geng Chen, Weili Lin, Pew Thian Yap, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


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.

Original languageEnglish
Article number8691605
Pages (from-to)2717-2725
Number of pages9
JournalIEEE Transactions on Medical Imaging
Issue number12
Publication statusPublished - 2019 Dec


  • Graph CNN
  • adversarial learning
  • diffusion MRI
  • early brain development
  • longitudinal prediction

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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