Difference between revisions of "2011 Winter Project Week:DTIPrepDocumentation"
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==Motivations: Automated Diffusion Weighted Imaging Quality Control == | ==Motivations: Automated Diffusion Weighted Imaging Quality Control == | ||
| − | Diffusion Tensor Imaging (DTI) has become an important MRI procedure to investigate the integrity of white matter in brain in vivo. DTI is estimated from a series of acquired Diffusion Weighted Imaging (DWI) volumes. Unfortunately, the DWIs suffer from a lots of artifacts because of the inherent low SNR and long scanning time of multiple directional encoding. Thus, it necessitates the development of Quality Control (QC) as the automated tool for DWIs. | + | Diffusion Tensor Imaging (DTI) has become an important MRI procedure to investigate the integrity of white matter in brain in vivo. DTI is estimated from a series of acquired Diffusion Weighted Imaging (DWI) volumes. Unfortunately, the DWIs suffer from a lots of artifacts because of the inherent low SNR and long scanning time of multiple directional encoding. Thus, it necessitates the development of Quality Control (QC) as the automated tool for DWIs. In addition, a number of artifacts are only detected in the DTI data level which means can not be recognized using voxel-wise knowledge. For example, residual artifact presence after DWI QC which shows a reddish appearance for DTIs. |
==Key Investigators== | ==Key Investigators== | ||
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<h3>Objective</h3> | <h3>Objective</h3> | ||
| − | We | + | We have introduced new automated QC tool called DTIprep that includes the pipeline steps of image information checking, diffusion vectors checking, slice-wise intensity checking, interlace-wise intensity checking, eddy-motion correction and gradient-wise information checking. The entire checking processes are doing against the DWI acquired protocol and the QCed result are reported and they are formatted in xml format which helps to modify and reuse their contexts. Thus, it is needed to check the used QC result and input DTI are matched. In addition, we are developing DTIPrep to integrity the visual checking considering into the automated QCed result. In order to produce robust and consistence results, DTIPrep should reconsider another way of the pipeline procedures on the updated diffusion gradients. Moreover, we are aiming to introduce new measurement of DTIs properties to detect residual artifacts presence after DWI QC. |
| + | |||
</div> | </div> | ||
Revision as of 22:20, 12 May 2011
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Diffusion Weighted Imaging and Diffusion Tensor Imaging Quality Control
Motivations: Automated Diffusion Weighted Imaging Quality Control
Diffusion Tensor Imaging (DTI) has become an important MRI procedure to investigate the integrity of white matter in brain in vivo. DTI is estimated from a series of acquired Diffusion Weighted Imaging (DWI) volumes. Unfortunately, the DWIs suffer from a lots of artifacts because of the inherent low SNR and long scanning time of multiple directional encoding. Thus, it necessitates the development of Quality Control (QC) as the automated tool for DWIs. In addition, a number of artifacts are only detected in the DTI data level which means can not be recognized using voxel-wise knowledge. For example, residual artifact presence after DWI QC which shows a reddish appearance for DTIs.
Key Investigators
- UIowa: Hans Johnson, Mark Scully, Joy Matsui
- UNC: Martin Styner, Clement Vachet, Mahshid Farzinfar, Cheryl Dietrich
Objective
We have introduced new automated QC tool called DTIprep that includes the pipeline steps of image information checking, diffusion vectors checking, slice-wise intensity checking, interlace-wise intensity checking, eddy-motion correction and gradient-wise information checking. The entire checking processes are doing against the DWI acquired protocol and the QCed result are reported and they are formatted in xml format which helps to modify and reuse their contexts. Thus, it is needed to check the used QC result and input DTI are matched. In addition, we are developing DTIPrep to integrity the visual checking considering into the automated QCed result. In order to produce robust and consistence results, DTIPrep should reconsider another way of the pipeline procedures on the updated diffusion gradients. Moreover, we are aiming to introduce new measurement of DTIs properties to detect residual artifacts presence after DWI QC.
Approach, Plan
Update the NITRC media wiki pages for DTIPrep to match the current version of the program. Use the updated wiki as a basis for a tutorial. Coordinate efforts with Cheryl Dietrich at UNC.
Progress
A detailed pdf on DTIPrep documentation has been uploaded from UNC. Work has started on creating a NAMIC-style ppt tutorial and will be completed soon. This tutorial will be submitted for the June tutorial contest.
