Abstract
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.
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Acknowledgements
This work was supported in part by NIH grants (NS093842, EB022880, EB006733, EB009634, AG041721, MH100217, and AA012388), an NSFC grant (11671022, China), and Institute for Information & communications Technology Promotion (IITP) grant (MSIT, 2018-2-00861, Intelligent SW Technology Development for Medical Data Analysis, South Korea).
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Kim, J., Hong, Y., Chen, G., Lin, W., Yap, PT., Shen, D. (2019). Graph-Based Deep Learning for Prediction of Longitudinal Infant Diffusion MRI Data. In: Bonet-Carne, E., Grussu, F., Ning, L., Sepehrband, F., Tax, C. (eds) Computational Diffusion MRI. MICCAI 2019. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-030-05831-9_11
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DOI: https://doi.org/10.1007/978-3-030-05831-9_11
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