Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Patch-Based Techniques in Medical Imaging - 3rd International Workshop, Patch-MI 2017 Held in Conjunction with MICCAI 2017, Proceedings |
| Publisher | Springer Verlag |
| Pages | 65-73 |
| Number of pages | 9 |
| Volume | 10530 LNCS |
| ISBN (Print) | 9783319674339 |
| DOIs | |
| Publication status | Published - 2017 |
| Externally published | Yes |
| Event | 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 Sep 14 → 2017 Sep 14 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 10530 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 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 |
|---|---|
| Country | Canada |
| City | Quebec City |
| Period | 17/9/14 → 17/9/14 |
Fingerprint
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Cite this
Learning-based estimation of functional correlation tensors in white matter for early diagnosis of mild cognitive impairment. / Zhang, Lichi; Zhang, Han; Chen, Xiaobo; Wang, Qian; Yap, Pew Thian; Shen, Dinggang.
Patch-Based Techniques in Medical Imaging - 3rd International Workshop, Patch-MI 2017 Held in Conjunction with MICCAI 2017, Proceedings. Vol. 10530 LNCS Springer Verlag, 2017. p. 65-73 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10530 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Learning-based estimation of functional correlation tensors in white matter for early diagnosis of mild cognitive impairment
AU - Zhang, Lichi
AU - Zhang, Han
AU - Chen, Xiaobo
AU - Wang, Qian
AU - Yap, Pew Thian
AU - Shen, Dinggang
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85029457589&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85029457589&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-67434-6_8
DO - 10.1007/978-3-319-67434-6_8
M3 - Conference contribution
AN - SCOPUS:85029457589
SN - 9783319674339
VL - 10530 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 65
EP - 73
BT - Patch-Based Techniques in Medical Imaging - 3rd International Workshop, Patch-MI 2017 Held in Conjunction with MICCAI 2017, Proceedings
PB - Springer Verlag
ER -