Difference between revisions of "2011 Winter Project Week:DTIPrepDocumentation"
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| − | ==Motivations: 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. 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 their context. We have developed the tool to integrity the visual checking result into the automated QCed result. | + | ==Motivations: 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. 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 their context. We have developed the tool to integrity the visual checking result into the automated QCed result. == |
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Revision as of 20:48, 12 May 2011
Home < 2011 Winter Project Week:DTIPrepDocumentation
Diffusion Weighted Imaging and Diffusion Tensor Imaging Quality Control
Motivations: 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. 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 their context. We have developed the tool to integrity the visual checking result into the automated QCed result.
Key Investigators
- UIowa: Hans Johnson, Mark Scully, Joy Matsui
- UNC: Martin Styner, Clement Vachet, Mahshid Farzinfar, Cheryl Dietrich
Objective
We are aiming to develop Quality Control (QC) procedures for Diffusion Weighted Imaging such that the artifacts are detected and corrected. The QC procedures are implemented in the open source tool DTIPrep and includes
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.
