2011 Winter Project Week:DTIPrepDocumentation - Revision history

MahshidLightMoon: /* Motivations: Automated Diffusion Weighted Imaging Quality Control */

2011-06-04T23:40:52Z

Motivations: Automated Diffusion Weighted Imaging Quality Control

MahshidLightMoon: /* Key Investigators */

2011-05-30T18:51:46Z

Key Investigators

MahshidLightMoon: /* Key Investigators */

2011-05-30T18:51:05Z

Key Investigators

MahshidLightMoon: /* Key Investigators */

2011-05-30T18:49:29Z

Key Investigators

MahshidLightMoon: /* Key Investigators */

2011-05-30T18:22:51Z

Key Investigators

MahshidLightMoon: /* Key Investigators */

2011-05-30T18:17:22Z

Key Investigators

MahshidLightMoon: /* Motivations & Objectives: Automated Diffusion Weighted Imaging Quality Control */

2011-05-30T15:48:09Z

Motivations & Objectives: Automated Diffusion Weighted Imaging Quality Control

MahshidLightMoon: /* Motivations: Automated Diffusion Weighted Imaging Quality Control */

2011-05-30T15:44:55Z

Motivations: Automated Diffusion Weighted Imaging Quality Control

MahshidLightMoon: /* Motivations: Automated Diffusion Weighted Imaging Quality Control */

2011-05-30T15:08:15Z

Motivations: Automated Diffusion Weighted Imaging Quality Control

MahshidLightMoon at 14:58, 30 May 2011

2011-05-30T14:58:03Z

@@ Line 7: / Line 7: @@
 ==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. We have developed a tool called DTIPrep which is fully open source. In the DTIPrep framework, the following checking procedures are applied within a pipelining structure and described by a protocoling file. These pipeline procedures 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 our current study, we are focusing on extending DTIPrep to detect artifacts through DTI data. According to our experiments, there are a number of artifacts which can not be detected from the DWIs using only voxel-wise knowledge. For example, residual artifact presence after DWI QC which shows a reddish appearance for DTIs.
+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 developed a tool called DTIPrep which is fully open source. In the DTIPrep framework, the following checking procedures are applied within a pipelining structure and described by a protocoling file. These pipeline procedures 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 our current study, we are focusing on extending DTIPrep to detect artifacts through DTI data. According to our experiments, there are a number of artifacts which can not be detected from the DWIs using only voxel-wise knowledge. For example, residual artifact presence after DWI QC which shows a reddish appearance for DTIs.
 ==Key Investigators==

@@ Line 45: / Line 45: @@
 '''*''' Doing visual checking after QC and then asking user whether further QC is applied for updated gradients or the QCed result is just saved.
-'''*''' Training and testing the new entropy-based measurement of PD histogram within gray-matter, white-matter and the entire image.
+'''*''' Training and testing the new entropy-based measurement of PD histogram within gray-matter, white-matter areas and the entire image.
 </div>

@@ Line 43: / Line 43: @@
 '''*''' All the pipeline procedures of DWI QC have been implemented successfully. DTIPrep generates QC report in xml format and with facilities of reloading QC result and checking mismatching between the image and corresponding reloaded QC report.
-'''*''' Doing visual checking after QC and then asking user whether further QC is applied for updated gradients or the QCed result just saved.
+'''*''' Doing visual checking after QC and then asking user whether further QC is applied for updated gradients or the QCed result is just saved.
 '''*''' Training and testing the new entropy-based measurement of PD histogram within gray-matter, white-matter and the entire image.

@@ Line 41: / Line 41: @@
 <h3>Progress</h3>
-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.
+'''*''' All the pipeline procedures of DWI QC have been implemented successfully. DTIPrep generates QC report in xml format and with facilities of reloading QC result and checking mismatching between the image and corresponding reloaded QC report.
 </div>
@@ Line 52: / Line 56: @@
 </div>
 -->
 ==References==
 '''[1]''' Z Liu, Y Wang, G Gerig, S Gouttard, R Tao, T Fletcher, M Styner: Quality control of diffusion weighted images. Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol: 7628, pg: 17, 2010.

@@ Line 30: / Line 30: @@
 <h3>Approach, Plan</h3>
-'''1.''' Checking size, origin, spacing of image, checking b-values, measurement frames and diffusion sensitizing directions. Applying statistical analysis on computed intensity-based correlations between successive slices and furthermore against interleaved parts of each gradient. Applying rigid and affine registration to correct and detect the motion artifacts. Generating xml QC report file. More details are found in
+'''1.''' Checking size, origin, spacing of image, checking b-values, measurement frames and diffusion sensitizing directions. Applying statistical analysis on computed intensity-based correlations between successive slices and furthermore against interleaved parts of each gradient. Applying rigid and affine registration to correct and detect the motion artifacts. Generating xml QC report file. More details are found in [1].
 '''2.''' Describing each gradient as "included" or "excluded" after not only applying QC but also visual checking analysis and defining further QC when some gradients get "included" in the visual checking step.
@@ Line 52: / Line 52: @@
 </div>
 -->

@@ Line 29: / Line 29: @@
 <h3>Approach, Plan</h3>
-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.
+'''1.''' Checking size, origin, spacing of image, checking b-values, measurement frames and diffusion sensitizing directions. Applying statistical analysis on computed intensity-based correlations between successive slices and furthermore against interleaved parts of each gradient. Applying rigid and affine registration to correct and detect the motion artifacts. Generating xml QC report file. More details are found in
 </div>

@@ Line 6: / Line 6: @@
 </gallery>
-==Motivations & Objectives: 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. We have developed a tool called DTIPrep which is fully open source. In the DTIPrep framework, the following checking procedures are applied within a pipelining structure and described by a protocoling file. These pipeline procedures 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 our current study, we are focusing on extending DTIPrep to detect artifacts through DTI data. According to our experiments, there are a number of artifacts which can not be detected from the DWIs using only voxel-wise knowledge. For example, residual artifact presence after DWI QC which shows a reddish appearance for DTIs.

@@ Line 2: / Line 2: @@
 <gallery>
 Image:PW-SLC2011.png|[[2011_Winter_Project_Week#Projects|Projects List]]
-Image:Screenshot.png|Diffusion Weighted Imaging and Diffusion Tensor Imaging Quality Control
+Image:Screenshot.png|Diffusion Weighted Imaging and Diffusion Tensor Imaging Quality Control_DTIPrep
-Image:753361684_3.png|Diffusion Weighted Imaging and Diffusion Tensor Imaging Quality Control ( 3D Gradients view)
+Image:753361684_3.png|3D view of gradients before and after Quality Control procedures
 </gallery>