It has been recently demonstrated that the local BOLD signals in resting-state fMRI (rs-fMRI) can be captured for the white matter (WM) by functional correlation tensors (FCTs). FCTs provide similar orientation information as diffusion tensors (DTs), and also functional information concerning brain dynamics. However, FCTs are susceptible to noise due to the low signal-to-noise ratio nature of WM BOLD signals. Here we introduce a robust FCT estimation method to facilitate individualized diagnosis. First, we develop a noise-tolerating patch-based approach to measure spatiotemporal correlations of local BOLD signals. Second, it is also enhanced by DTs predicted from the input rs-fMRI using a learning-based regression model. We evaluate our trained regressor using the high-resolution HCP dataset. The regressor is then applied to estimate the robust FCTs for subjects in the ADNI2 dataset. We demonstrate for the first time the disease diagnostic value of robust FCTs.
|Title of host publication
|Patch-Based Techniques in Medical Imaging - 3rd International Workshop, Patch-MI 2017 Held in Conjunction with MICCAI 2017, Proceedings
|Yiqiang Zhan, Wenjia Bai, Guorong Wu, Pierrick Coupe, Brent C. Munsell, Gerard Sanroma
|Number of pages
|Published - 2017
|3rd International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2017 held in conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: 2017 Sept 14 → 2017 Sept 14
|Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
|3rd International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2017 held in conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
|17/9/14 → 17/9/14
Bibliographical notePublisher Copyright:
© Springer International Publishing AG 2017.
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science