Name
Abstract | ||
|---|---|---|
Image distortions caused by eddy current and patient motion have been two major sources of the mis-registration issues in diffusion tensor imaging (DTI). Numerous registration methods have been proposed to correct them. However, quality control of DTI remains an important issue, because we rarely report how much mis-registration existed and how well they were corrected. In this paper, we propose a method for quantitative reporting of DTI data quality. This registration method minimizes a cost function based on mean square tensor fitting errors. Registration with twelve-parameter full affine transformation is used. From the registration result, distortion and motion parameters are estimated. Because the translation parameters involve both eddy-current-induced image translation and the patient motion, by analyzing the transformation model, we separate them by removing the contributions that are linearly correlated with diffusion gradients. We define the metrics measuring the amounts of distortion, rotation, translation. We tested our method on a database with 64 subjects and found the statistics of each of metrics. Finally we demonstrate that how these statistics can be used for assessing the data quality quantitatively in several examples. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1117/12.911129 | Proceedings of SPIE |
Keywords | Field | DocType |
Diffusion Tensor Imaging,Registration,Motion,Eddy current,Distortion,Affine | Affine transformation,Image translation,Computer vision,Mean square,Diffusion MRI,Data quality,Tensor,Artificial intelligence,Quantitative assessment,Distortion,Physics | Conference |
Volume | ISSN | Citations |
8314 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yue Li | 1 | 12 | 1.63 |
| Hangyi Jiang | 2 | 550 | 31.16 |
| Susumu Mori | 3 | 645 | 43.22 |