Name
Abstract | ||
|---|---|---|
Diffusion tensor magnetic resonance imaging (DT-MRI) is a relatively new imaging modality in the field of medical imaging. This modality of imaging allows one to capture the structural connectivity if any between functionally meaningful regions for example, in the brain. The data however can be noisy and requires restoration. In this paper, we present a novel unified model for simultaneous smoothing and estimation of diffusion tensor field from DT-MRI. The diffusion tensor field is estimated directly from the raw data with Lp smoothness and positive definiteness constraints. The data term we employ is from the original Stejskal-Tanner equation instead of the linearized version as usually done in literature. In addition, we use Cholesky decomposition to ensure positive definiteness of the diffusion tensor. The unified model is discretized and solved numerically using limited memory Quasi-Newton method. Both synthetic and real data experiments are shown to depict the algorithm performance. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/CVPR.2003.1211390 | CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition |
Keywords | Field | DocType |
novel unified model,simultaneous smoothing,diffusion tensor magnetic resonance,diffusion tensor field,new imaging modality,positive definiteness,medical imaging,data term,real data experiment,diffusion tensor,raw data,mathematics,newton method,unified model,biochemistry,image restoration,quasi newton method,tensile stress,magnetic resonance imaging,cholesky decomposition,magnetic resonance image,diffusion tensor imaging,anisotropic magnetoresistance,computed tomography,positive definite | Discretization,Diffusion MRI,Medical imaging,Mathematical analysis,Computer science,Tensor field,Smoothing,Positive definiteness,Cholesky decomposition,Newton's method | Conference |
ISSN | ISBN | Citations |
1063-6919 | 0-7695-1900-8 | 8 |
PageRank | References | Authors |
1.70 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhizhou Wang | 1 | 224 | 19.85 |
| B.C. Vemuri | 2 | 4208 | 536.42 |
| Yunmei Chen | 3 | 8 | 1.70 |
| T. Mareci | 4 | 42 | 5.79 |