GTRACT V4 - Revision history

Matsuij: /* Anatomical B-Spline */

2010-12-21T06:20:03Z

Anatomical B-Spline

Matsuij: /* Fiber Tracking */

2010-12-21T06:06:45Z

Fiber Tracking

Matsuij: /* Resampling Images */

2010-12-21T05:51:40Z

Resampling Images

Matsuij: /* Invert B-Spline */

2010-12-21T05:50:30Z

Invert B-Spline

Matsuij: /* Anatomical B-Spline */

2010-12-21T05:48:02Z

Anatomical B-Spline

Matsuij: /* Anatomical Rigid */

2010-12-21T05:47:01Z

Anatomical Rigid

Matsuij: /* Copy Reference Image Orientation */

2010-12-21T05:46:12Z

Copy Reference Image Orientation

Matsuij: /* Compute Anisotropy Images */

2010-12-21T05:45:17Z

Compute Anisotropy Images

Matsuij: /* DTI Image Co-Registration */

2010-12-21T05:43:54Z

DTI Image Co-Registration

Matsuij: /* GTRACT Programs */

2010-12-21T05:37:41Z

GTRACT Programs

@@ Line 151: / Line 151: @@
 ===Anatomical B-Spline===
-The B-SPline registration places a low dimensional grid in the image that is deformed. The B-Spline registration works best if the anatomical image has been skull stripped and a rigid registration is used as the staring point for the registration. The user can specify the number of control points to be used in the registration and the maximum distance that each control point should move. In general the amount of motion in the slice selection and read-out directions direction should be kept low. The distortion is in the phase encoding direction in the images. A bulk transform can be applied to the B-Spline transform to initialize the transform. This should be the rigid transform generated from the previous step for the optimal results. The program '''gtractCoRegAnatomyBspline''' performs this registration step and a detailed list of parameters can be found here, [[gtractCoRegAnatomy]]
+The B-SPline registration places a low dimensional grid in the image that is deformed. The B-Spline registration works best if the anatomical image has been skull stripped and a rigid registration is used as the staring point for the registration. The user can specify the number of control points to be used in the registration and the maximum distance that each control point should move. In general the amount of motion in the slice selection and read-out directions direction should be kept low. The distortion is in the phase encoding direction in the images. A bulk transform can be applied to the B-Spline transform to initialize the transform. This should be the rigid transform generated from the previous step for the optimal results. The program '''gtractCoRegAnatomy''' performs this registration step and a detailed list of parameters can be found here, [[gtractCoRegAnatomy]]
 '''Example:'''

@@ Line 220: / Line 220: @@
 # Fast Marching Tracking
-The output of the fiber tracking is vtkPolyData (i.e. Polylines) that can be loaded into Slicer3 for visualization. The poly data can be saved in either old VTK format files (.vtk) or in the new VTK XML format (.xml). Currently, Slicer3 only accepts the old .vtk file format. The polylines contain point data that defines ther Tensor at each point along the fiber tract. This can then be used to rendered as glyphs in Slicer3 and can be used to define severeal scalar measures without referencing back to the anisotropy images.
+The output of the fiber tracking is vtkPolyData (i.e. Polylines) that can be loaded into Slicer3 for visualization. The poly data can be saved in either old VTK format files (.vtk) or in the new VTK XML format (.xml). Currently, Slicer3 only accepts the old .vtk file format. The polylines contain point data that defines the Tensor at each point along the fiber tract. This can then be used to be rendered as glyphs in Slicer3 and can be used to define several scalar measures without referencing back to the anisotropy images.
 ===Free Tracking===
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 ===Streamline Tracking===
-The streamlines algorithm is a direct implementation of the method original proposed by Basser et al. The tracking follows the primarty eigenvector. The tracking begins with seed points in the starting region. Only those voxels above the specified anisotropy threshold in the starting region are used as seed points. Tracking terminates either by reaching the ending region or reaching some stopping criteria. Stopping criteria are specified using the following parameters:
+The streamlines algorithm is a direct implementation of the method original proposed by Basser et al. The tracking follows the primary eigenvector. The tracking begins with seed points in the starting region. Only those voxels above the specified anisotropy threshold in the starting region are used as seed points. Tracking terminates either by reaching the ending region or reaching some stopping criteria. Stopping criteria are specified using the following parameters:
-* '''Tracking Threshold''': Anistropy values of the next point along the path
+* '''Tracking Threshold''': Anisotropy values of the next point along the path
 * '''Curvature''': Angle of the  next tracking direction relative to the current path direction
 * '''Max Length''': Length of the track in voxels
@@ Line 266: / Line 266: @@
 ===Graph Search Tracking===
-The Graph Search tracking is the first step in the full GTRACT algorithm developed by Cheng et al. ([http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WNP-4JVTCDN-1&_user=440026&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000020939&_version=1&_urlVersion=0&_userid=440026&md5=a58d1d1ffaf9741f2d95aa294e017b17 NeuroImage 31(3): 1075-1085, 2006]). for finding the tracks in a tensor image. This method was developed to
+The Graph Search tracking is the first step in the full GTRACT algorithm developed by Cheng et al. ([http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WNP-4JVTCDN-1&_user=440026&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000020939&_version=1&_urlVersion=0&_userid=440026&md5=a58d1d1ffaf9741f2d95aa294e017b17 NeuroImage 31(3): 1075-1085, 2006]) for finding the tracks in a tensor image. This method was developed to
-generate fibers in a Tensor representation where crossing fibers occur. The graph search algorithm follows the primary eigenvector in non-ambiguous regions and utilizes branching and a graph search algorithm in ambigous regions. Ambiguous tracking regions are defined based on two criteria:
+generate fibers in a Tensor representation where crossing fibers occur. The graph search algorithm follows the primary eigenvector in non-ambiguous regions and utilizes branching and a graph search algorithm in ambiguous regions. Ambiguous tracking regions are defined based on two criteria:
-*'''Branching AI Threshold''': Anistropy values below this value and above the tracking threshold
+*'''Branching AI Threshold''': Anisotropy values below this value and above the tracking threshold
 * '''Curvature Major Eigen''': Angles of the primary eigenvector direction and the current tracking direction
@@ Line 298: / Line 298: @@
-In this method, the fiber tracking follows the primary eigenvector direction unless it deviates from the guide fiber track by a angle greater than that specified by the '--guidedCurvatureThreshold' parameter. The program [[gtractFiberTracking]] his algorithm. The user must specify the guide fiber when running this program.
+In this method, the fiber tracking follows the primary eigenvector direction unless it deviates from the guide fiber track by a angle greater than that specified by the '--guidedCurvatureThreshold' parameter. The program [[gtractFiberTracking]] uses this algorithm. The user must specify the guide fiber when running this program.
@@ Line 320: / Line 320: @@
 feature of this version is the inclusion of anisotropy in the cost function calculation. The user has the ability to weight the contribution of the anisotropy weight.
-This algorithm is broken into two parts. The first phase of the algorithm generates a cost image. This starts with a seed region specified by the user. The cost of the path tracking from the seed region to every voxel in the brain is computed. The cost is based on the direction of the front, the primary eigenvector direction, and the anisotropy value. The user can adjust the weight of the anisotropy value using the '--anisotropyWeight' flag. The program [[gtractCostFastMarching]] is responsible for the generation of the cost image. This may take several minutes to generate and increases with the size of the seed region. Currently, both a cost and spped image are generated. The speed image is not used in the fiber tracking phase and will become an optional parameter in future releases. Currently, it is a required parameter.
+This algorithm is broken into two parts. The first phase of the algorithm generates a cost image. This starts with a seed region specified by the user. The cost of the path tracking from the seed region to every voxel in the brain is computed. The cost is based on the direction of the front, the primary eigenvector direction, and the anisotropy value. The user can adjust the weight of the anisotropy value using the '--anisotropyWeight' flag. The program [[gtractCostFastMarching]] is responsible for the generation of the cost image. This may take several minutes to generate and increases with the size of the seed region. Currently, both a cost and speed image are generated. The speed image is not used in the fiber tracking phase and will become an optional parameter in future releases. Currently, it is a required parameter.
 '''Example:'''

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 Three programs have been written to resample the various types of images that may be encountered in diffusion tensor analysis. All of the programs accept only ITK transforms and in particular '''Versor Rigid'' or '''B-Spline''' transforms that are described above.
-* [[gtractResampleCodeImage]] - Resamples a mask or label - Image pixel type is signed short and neareast neigbor interpolation is used
+* [[gtractResampleCodeImage]] - Resamples a mask or label - Image pixel type is signed short and nearest neighbor interpolation is used
 '''Example:'''

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 ===Invert B-Spline===
-Since the B-Spline transform cannot be directly inverted, we have implemented a thin plate spline approximation to its inverse. This can be created yusing the gtractInvertBspline. The X, Y and Z grid specifiy the grid utilized to sample the B-Spline transform. The parameters for [[gtractInvertBSplineTransform]] can be obtained using the '-help' option. The inverse needs to be computed to map fiber tracts from the DTI space to the anatomical image and to map regions of interest from the anatomical image onto the diffusion weighted images using this non-linear transform that accounts in part for susecptility artifacts.
+Since the B-Spline transform cannot be directly inverted, we have implemented a thin plate spline approximation to its inverse. This can be created using the gtractInvertBspline. The X, Y and Z grids specify the grid utilized to sample the B-Spline transform. The parameters for [[gtractInvertBSplineTransform]] can be obtained using the '-help' option. The inverse needs to be computed to map fiber tracts from the DTI space to the anatomical image and to map regions of interest from the anatomical image onto the diffusion weighted images using this non-linear transform that accounts in part for susceptibility artifacts.
 '''Example'''

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 ===Anatomical B-Spline===
-The B-SPline registration places a low dimensional grid in the image which is deformed. The B-Spline registratiuon works best if the anatomical image has been skull stripped and a rigid registration is used as the staring point for the registration. The user can specify the number of control points to be used in the registration and the maximum distance that each control point should move. In general the amount of motion in the slice selection and read-out directions direction should be kept low. The distortion is in the phase encoding direction in the images. A bulk transform can be applied to the B-Spline transform to initialize the transform. This should be the rigid transform generated from the previous step for the optimal results. The program '''gtractCoRegAnatomyBspline''' performs this registration step and a detailed list of parameters can nbe found here, [[gtractCoRegAnatomy]]
+The B-SPline registration places a low dimensional grid in the image that is deformed. The B-Spline registration works best if the anatomical image has been skull stripped and a rigid registration is used as the staring point for the registration. The user can specify the number of control points to be used in the registration and the maximum distance that each control point should move. In general the amount of motion in the slice selection and read-out directions direction should be kept low. The distortion is in the phase encoding direction in the images. A bulk transform can be applied to the B-Spline transform to initialize the transform. This should be the rigid transform generated from the previous step for the optimal results. The program '''gtractCoRegAnatomyBspline''' performs this registration step and a detailed list of parameters can be found here, [[gtractCoRegAnatomy]]
 '''Example:'''

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 ===Anatomical Rigid===
-The rigid registration uses a mutual information metric to align the b0 image with the anatomical image. A reasonable set of parameters is specified as the default set of parameters. These were initially tested on a data from both a Siemens 1.5T and 3.0T scanners. Here are the complete set of parameters for [[gtractCoRegAnatomy]]
+The rigid registration uses a mutual information metric to align the b0 image with the anatomical image. A reasonable set of parameters is specified as the default set of parameters. These were initially tested on a data from both a Siemens 1.5T and 3.0T scanners. Here is the complete set of parameters for [[gtractCoRegAnatomy]].
 '''Example'''

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 ===Copy Reference Image Orientation===
-Currently, the registration process requires that the diffusion weighted images and the anatomical images have the same image orientation (i.e. Axial, Coronal, Sagittal). It is suggested that you copy the image orientation from the diffusion weighted images and apply this to the anatomical image. This image can be subsequently removed after the registration step is complete. We anticipate that this limitation will be removed in future versions of the registration programs. The program '''gtractCopyImageOrientation''' is used for this step. The complete command line arguements for [[gtractCopyImageOrientation]] can be generated using the '--help' flag.
+Currently, the registration process requires that the diffusion weighted images and the anatomical images have the same image orientation (i.e. Axial, Coronal, Sagittal). It is suggested that you copy the image orientation from the diffusion weighted images and apply this to the anatomical image. This image can be subsequently removed after the registration step is complete. We anticipate that this limitation will be removed in future versions of the registration programs. The program '''gtractCopyImageOrientation''' is used for this step. The complete command line arguments for [[gtractCopyImageOrientation]] can be generated using the '--help' flag.
   % gtractCopyImageOrientation --inputVolume T1_ACPC_clip.nii.gz \

@@ Line 106: / Line 106: @@
 == Compute Anisotropy Images ==
-Anistropy mapping can be used to generate the following diffusion tensor indices:
+Anisotropy mapping can be used to generate the following diffusion tensor indices:
-* Fractional Anistroy (FA)
+* Fractional Anisotropy (FA)
-* Relative Anistroy (RA)
+* Relative Anisotropy (RA)
 * Volume Ratio (VR)
 * Lattice Index (LI)

@@ Line 57: / Line 57: @@
 == DTI Image Co-Registration ==
-Once the images are in NRRD format the next step in the analysis pipeline is the elimination of motion and eddy current artifacts between the diffusion weighted images. The program for this step of the analysis is [[gtractCoregBvalues]]. This program will support registration between two diffusion series in the case where you would like to average this data across runs. It also would allow for co-regsitration with a T2 weighted image or field map in the same plane as the DTI data.
+Once the images are in NRRD format the next step in the analysis pipeline is the elimination of motion and eddy current artifacts between the diffusion weighted images. The program for this step of the analysis is [[gtractCoregBvalues]]. This program will support registration between two diffusion series in the case where you would like to average this data across runs. It also would allow for co-registration with a T2 weighted image or field map in the same plane as the DTI data.
-A mutual information metric cost function is used for the registration because of the differences in the signal intensity as a result of the diffusion gradients. The fixed image for the registration should be a b0 image. The user has the option to perform a rigid or a full affine registration. The full affine allows the registration procedure to correct for eddy current distortions that may exist in the data. This is specified with the '''eddyCurrentCorrection''' option. In general the standard parameters work fairly well for a rigid registration, and you probably just need to specify the images for registration. When the eddyCurrent Correction is specified, then we have had to adjusrt the '''relaxationFactor''' and '''maximumStepSize''' parameters. We typically use values of around 0.25 for the relaxation factor and 0.1 for the maximum step size when this option is turned on.
+A mutual information metric cost function is used for the registration because of the differences in the signal intensity as a result of the diffusion gradients. The fixed image for the registration should be a b0 image. The user has the option to perform a rigid or a full affine registration. The full affine allows the registration procedure to correct for eddy current distortions that may exist in the data. This is specified with the '''eddyCurrentCorrection''' option. In general the standard parameters work fairly well for a rigid registration, and you probably just need to specify the images for registration. When the eddyCurrent Correction is specified, then we have had to adjust the '''relaxationFactor''' and '''maximumStepSize''' parameters. We typically use values of around 0.25 for the relaxation factor and 0.1 for the maximum step size when this option is turned on.
   % gtractCoregBvalues --fixedImageVolume baseImage.nhdr --movingImageVolume baseImage.nhdr --fixedImageIndex 0 \

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 * [[gtractResampleB0|gtractResampleB0 Documentation]]
 * [[gtractTensor|gtractTensor Documentation]]
 == DTI Image Co-Registration ==