Difference between revisions of "Projects:DTI DWI QualityControl"
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Revision as of 19:22, 30 May 2011
Home < Projects:DTI DWI QualityControlBack to UNC Algorithms
Diffusion Tensor and Diffusion Weighted Imaging Quality Control
DWI data suffers from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies. We are developing a framework for automatic DWI and DTI quality assessment and correction. We developed a tool called DTIPrep which pipelines the QC steps with designated protocol use and report generation.
Description
|3D view of gradients before and after Quality Control procedures
Publications
Key Investigators
- UNC Algorithms: Mahshid Farzinfar, Zhexing Liu, Martin Styner, Clement Vachet
- Utah Algorithms: Tom Fletcher, Ross Whitaker, Guido Gerig, Sylvain Gouttard
Links
- DTIPrep on NITRC
