Advanced diffusion models for tissue microstructure are widely employed to study brain disorders. However, these models usually require diffusion MRI (DMRI) data with densely sampled q-space, which is prohibitive in clinical settings. This problem can be resolved by using deep learning techniques, which learn the mapping between sparsely sampled q-space data and the high-quality diffusion microstructural indices estimated from densely sampled data. However, most existing methods simply view the input DMRI data as a vector without considering data structure in the q-space. In this paper, we propose to overcome this limitation by representing DMRI data using graphs and utilizing graph convolutional neural networks to estimate tissue microstructure. Our method makes full use of the q-space angular neighboring information to improve estimation accuracy. Experimental results based on data from the Baby Connectome Project demonstrate that our method outperforms state-of-the-art methods both qualitatively and quantitatively.
|Title of host publication
|Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
|Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
|Springer Science and Business Media Deutschland GmbH
|Number of pages
|Published - 2020
|23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: 2020 Oct 4 → 2020 Oct 8
|Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
|23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
|20/10/4 → 20/10/8
Bibliographical noteFunding Information:
This work was supported in part by NIH grants (NS093842, EB006733, MH104324, and MH110274) and the efforts of the UNC/UMN Baby Connectome Project Consortium.
© 2020, Springer Nature Switzerland AG.
- Diffusion MRI
- Graph CNN
- Microstructure imaging
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science