Graph-Based Deep Learning for Prediction of Longitudinal Infant Diffusion MRI Data

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

Research output: Contribution to journalConference article

1 Citation (Scopus)


Diffusion MRI affords great value for studying brain development, owing to its capability in assessing brain microstructure in association with myelination. With longitudinally acquired pediatric diffusion MRI data, one can chart the temporal evolution of microstructure and white matter connectivity. However, due to subject dropouts and unsuccessful scans, longitudinal datasets are often incomplete. In this work, we introduce a graph-based deep learning approach to predict diffusion MRI data. The relationships between sampling points in spatial domain (x-space) and diffusion wave-vector domain (q-space) are harnessed jointly (x-q space) in the form of a graph. We then implement a residual learning architecture with graph convolution filtering to learn longitudinal changes of diffusion MRI data along time. We evaluate the effectiveness of the spatial and angular components in data prediction. We also investigate the longitudinal trajectories in terms of diffusion scalars computed based on the predicted datasets.

Original languageEnglish
Pages (from-to)133-141
Number of pages9
JournalMathematics and Visualization
Issue number226249
Publication statusPublished - 2019 Jan 1
Externally publishedYes
EventInternational Workshop on Computational Diffusion MRI, CDMRI 2018 held with International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain
Duration: 2018 Sep 202018 Sep 20


  • Brain development
  • Diffusion MRI
  • Graph convolution
  • Graph representation
  • Longitudinal prediction
  • Residual graph neural network

ASJC Scopus subject areas

  • Modelling and Simulation
  • Geometry and Topology
  • Computer Graphics and Computer-Aided Design
  • Applied Mathematics

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