Name
Abstract | ||
|---|---|---|
We develop a differential geometric framework for regularizing diffusion MRI data. The key idea is to model white matter fibers as 3D space curves and to then extend Parent and Zucker's 2D curve inference approach [8] by using a notion of co-helicity to indicate compatibility between fibre orientation estimates at each voxel with those in a local neighborhood. We argue that this provides several advantages over earlier regularization methods. We validate the approach quantitatively on a biological phantom and on synthetic data, and qualitatively on data acquired in vivo from a human brain. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11566465_16 | MICCAI |
Keywords | Field | DocType |
diffusion mri regularization,fibre orientation estimate,diffusion mri data,regularization method,approach quantitatively,local neighborhood,synthetic data,key idea,differential geometric framework,human brain,curve inference approach,diffusion mri | Voxel,Diffusion MRI,Pattern recognition,Computer science,Inference,Imaging phantom,Regularization (mathematics),Synthetic data,Artificial intelligence | Conference |
Volume | Issue | ISSN |
8 | Pt 1 | 0302-9743 |
ISBN | Citations | PageRank |
3-540-29327-2 | 7 | 0.83 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peter Savadjiev | 1 | 144 | 12.06 |
| Jennifer S W Campbell | 2 | 255 | 18.91 |
| G Bruce Pike | 3 | 699 | 132.31 |
| Kaleem Siddiqi | 4 | 3259 | 242.07 |