Name
Title | ||
|---|---|---|
Anisotropic Sampling Shape of White Matter Microstructure Cannot Cheat Diffusional Kurtosis. |
Abstract | ||
|---|---|---|
Diffusion kurtosis imaging is a newly developed diffusion magnetic resonance imaging technique, which is becoming increasingly valuable in clinical practice. Although low-resolution sampling is commonly used to compensate the unsteadiness of kurtosis estimation, the influence of the sampling shape has not been investigated. In this study, by using two different acquisition protocols, isotropic and anisotropic sampling voxels were acquired and their influence on various white matter structures was observed. Fiber tracking, T-tests, and correlation analysis were used to quantify the difference between the anisotropic and isotropic sampling. A significant difference (p < 0.01) was found in the fractional anisotropic level but not in kurtosis. The results presented here can provide a basis for higher resolution as well as higher quality kurtosis mapping, which may be of great significance in clinical examinations. |
Year | Venue | Keywords |
|---|---|---|
2016 | JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS | diffusion tensor imaging,diffusion kurtosis imaging,white matter,anisotropic sampling |
Field | DocType | Volume |
Statistical physics,Microstructure,Diffusion MRI,Anisotropy,White matter,Pattern recognition,Diffusion Kurtosis Imaging,Computer science,Sampling (statistics),Artificial intelligence,Kurtosis | Journal | 20 |
Issue | ISSN | Citations |
4 | 1343-0130 | 0 |
PageRank | References | Authors |
0.34 | 6 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuanyuan Chen | 1 | 1 | 1.38 |
| Xin Zhao | 2 | 6 | 4.16 |
| Miao Sha | 3 | 0 | 0.34 |
| Yanan Liu | 4 | 0 | 0.34 |
| jianguo ma | 5 | 0 | 0.68 |
| hongyan ni | 6 | 0 | 0.68 |
| Hongzhi Qi | 7 | 49 | 20.61 |
| Dong Ming | 8 | 105 | 51.47 |