Difference between revisions of "Projects:DTI DWI QualityControl"
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= Diffusion Tensor and Diffusion Weighted Imaging Quality Control = | = 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 | + | 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, called DTIPrep. DTIPrep as the first comprehensive and fully automatic pre-processing tool for DWI and DTI quality control can provide a crucial piece for robust DTI analysis studies. The protocoling, reporting, visual controlling and data correction capabilities are used to produce high consistence and inter-rater reliable QC results. This framework is organized by pipelines steps include: 1) image info checking, 2) diffusion info checking, 3) Slice-wise intensity checking, 4) Interlace-wise intensity checking, 5) Averaging baselines, 6) Eddy-motion correction, 7) gradient-wise checking, 8) Computing DTI measurements and saving. |
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| + | As further extended DTIprep, our experiments show that the residual artifacts presence after DTIPrep QC can be detected and corrected using knowledge of DTI. We introduce new entropy-based measurement of the DTI data from Principal Directions histogram of the entire image. After training our measurement, the QC is applied on assessing DTI information within gray-matter regions, white-matter areas or the entire image. | ||
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= Description = | = Description = | ||
| − | [[Image:Screenshot.png| | + | [[Image:Screenshot.png|400px|thumb|right|alt Diffusion Weighted Imaging and Diffusion Tensor Imaging Quality Control_DTIPrep]] |
| − | [[Image:753361684_3.png| | + | [[Image:753361684_3.png|400px|thumb|right|alt 3D view of gradients before and after Quality Control procedures]] |
= Publications = | = Publications = | ||
Revision as of 19:47, 30 May 2011
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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, called DTIPrep. DTIPrep as the first comprehensive and fully automatic pre-processing tool for DWI and DTI quality control can provide a crucial piece for robust DTI analysis studies. The protocoling, reporting, visual controlling and data correction capabilities are used to produce high consistence and inter-rater reliable QC results. This framework is organized by pipelines steps include: 1) image info checking, 2) diffusion info checking, 3) Slice-wise intensity checking, 4) Interlace-wise intensity checking, 5) Averaging baselines, 6) Eddy-motion correction, 7) gradient-wise checking, 8) Computing DTI measurements and saving.
As further extended DTIprep, our experiments show that the residual artifacts presence after DTIPrep QC can be detected and corrected using knowledge of DTI. We introduce new entropy-based measurement of the DTI data from Principal Directions histogram of the entire image. After training our measurement, the QC is applied on assessing DTI information within gray-matter regions, white-matter areas or the entire image.
Description
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
