Name
Title | ||
|---|---|---|
Differential information content in staggered multiple shell hardi measured by the tensor distribution function |
Abstract | ||
|---|---|---|
Diffusion tensor imaging has accelerated the study of brain connectivity, but single-tensor diffusion models are too simplistic to model fiber crossing and mixing. Hybrid diffusion imaging (HYDI) samples the radial and angular structure of local diffusion on multiple spherical shells in q-space, combining the high SNR and CNR achievable at low and high b-values, respectively. We acquired and analyzed human multi-shell HARDI at ultra-high field-strength (7 Tesla; b=1000, 2000, 3000 s/mm2). In experiments with the tensor distribution function (TDF), the b-value affected the intrinsic uncertainty for estimating component fiber orientations and their diffusion eigenvalues. We computed orientation density functions by least-squares fitting in multiple HARDI shells simultaneously. Within the range examined, higher b-values gave improved orientation estimates but poorer eigenvalue estimates; lower b-values showed opposite strengths and weaknesses. Combining these strengths, multiple-shell HARDI, especially with staggered angular sampling, outperformed single-shell scanning protocols, even when overall scanning time was held constant. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ISBI.2011.5872411 | ISBI |
Keywords | DocType | ISSN |
tdf,entropy,odf,staggered multiple shell hardi,multiple spherical shells,staggered angular sampling,hardi,diffusion tensor imaging,human multishell hardi,single-shell scanning protocols,least squares approximations,brain connectivity,biomedical mri,single-tensor diffusion models,brain,tensor distribution function,least-squares fitting,differential information content,diffusion eigenvalues,eigenvalues and eigenfunctions,multi-shell,medical image processing,component fiber orientations,orientation density functions,information content,eigenvalues,indexing terms,tensile stress,distribution functions,least squares fitting,imaging,distribution function,diffusion model,image resolution,least square | Conference | 1945-7928 E-ISBN : 978-1-4244-4128-0 |
ISBN | Citations | PageRank |
978-1-4244-4128-0 | 7 | 0.57 |
References | Authors | |
4 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liang Zhan | 1 | 145 | 24.82 |
| Alex D. Leow | 2 | 517 | 44.28 |
| Iman Aganj | 3 | 195 | 18.93 |
| Christophe Lenglet | 4 | 880 | 56.06 |
| Guillermo Sapiro | 5 | 14813 | 1051.92 |
| Essa Yacoub | 6 | 1800 | 107.62 |
| Noam Harel | 7 | 480 | 27.56 |
| Arthur W. Toga | 8 | 3128 | 261.46 |
| Paul Thompson | 9 | 3860 | 321.32 |