Projects:AtlasBasedDTIFiberAnalyzerFramework - Revision history

Styner at 20:29, 11 December 2012

2012-12-11T20:29:24Z

2012-12-11T20:28:52Z

Subjects fiber profile information via DTIAtlasFiberAnalyzer:

2012-12-11T20:28:42Z

Tractography within 3D Slicer with FiberViewerLight:

2012-12-11T20:28:15Z

DTI preprocessing- skull-stripping:

2012-12-11T20:27:49Z

DWI and DTI quality control:

2012-12-11T20:27:30Z

Statistical analysis performed by statistician:

2012-12-11T20:26:29Z

Statistical analysis performed by statistician:

2012-12-11T20:25:32Z

Tractography within 3D Slicer:

2012-12-11T20:25:21Z

Unbiased DTI atlas building or atlas mapping:

2012-12-11T20:24:57Z

Unbiased DTI atlas building or atlas mapping:

@@ Line 35: / Line 35: @@
 [[Image:NeonateAtlas_Tracts.png|thumb|right|300px|Fiber tracts defined in a DTI neonate atlas, DTI average of 270 individual subjects]]
-==='''Tractography within 3D Slicer  '''===
+==='''Tractography within 3D Slicer : '''===
 Tractography is performed on the DTI atlas to generate a template geometry for tracts of interest. Several methods can be used in that regard:
 * Single tensor-tractography Label seeding and ROI select
@@ Line 42: / Line 42: @@
 [[Image:Tract-stats.png|thumb|right|300px|White matter diffusion properties along fiber tract: Left: Uncinate fasiculus with coordinate origin plane, Right: FA mean and standard deviation as function of arc-length, starting at frontal region. Dots mark location of coordinate origin.]]
-==='''Fiber cleanup/clustering: '''===
+==='''Fiber cleanup/clustering with FiberViewerLight: '''===
 Tracts generated on the DTI atlas often need to be cleaned up. FiberViewerLight performs such clustering via length, gravity, hausdorff and mean based methods, but also a normalized cut algorithm on pairwise mean distances. FiberViewerLight also provides 3D fibers visualization and 3D plane selection, for future FA analysis along fibers.
 ==='''Subjects fiber profile information via DTIAtlasFiberAnalyzer: '''===

@@ Line 48: / Line 48: @@
 ==='''Subjects fiber profile information via DTIAtlasFiberAnalyzer: '''===
 Fiber tracts defined on the atlas are mapped to the individual subjects using previously computed deformations fields. Various tract-oriented scalar diffusion measures obtained from DTI brain images, are treated as a continuous function of white matter fibers' arc-length. To analyze the trend along a given fiber tract, a command line tool performs kernel regression on this data. Fiber profile information are gathered across subjects in spreadsheets for statistical analysis.
 ==='''Statistical analysis performed by statistician: '''===

@@ Line 35: / Line 35: @@
 [[Image:NeonateAtlas_Tracts.png|thumb|right|300px|Fiber tracts defined in a DTI neonate atlas, DTI average of 270 individual subjects]]
-==='''Tractography within 3D Slicer with FiberViewerLight: '''===
+==='''Tractography within 3D Slicer  '''===
 Tractography is performed on the DTI atlas to generate a template geometry for tracts of interest. Several methods can be used in that regard:
 * Single tensor-tractography Label seeding and ROI select

@@ Line 22: / Line 22: @@
 Skull-stripping is performed on DTI images and scalar maps. Several methods can be used in that regard:
 * Direct Otsu Thresholding
-* Masking using tissue label map generated by an intermediate atlas-based tissue segmentation, performed either on the idWI & B0 images or on the structural images (T1w & T2w).
+* Masking using tissue label map generated by an intermediate atlas-based tissue segmentation, performed either on the IdWI & B0 images or on the structural images (T1w & T2w).

@@ Line 15: / Line 15: @@
 The general framework entails the following steps:
-==='''DWI and DTI quality control: '''===
+==='''DWI and DTI quality control with DTIPrep: '''===
 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 developed a tool called DTIPrep which pipelines the QC steps with designated protocol use and report generation, and which generates DTI images and related scalar maps. The DTI QC is part of ongoing NAMIC research ([[Projects:DTI_DWI_QualityControl|More...]])

@@ Line 51: / Line 51: @@
 ==='''Statistical analysis performed by statistician: '''===
-Statistical analysis can be performed on fiber tracts to find cross-sectional or longitudinal intra- or interpatient differences. We currently employ the matlab based FADDT analysis tool. A corresponding wrapper application for integration into Slicer is in progress.
+Statistical analysis can be performed on fiber tracts to find cross-sectional or longitudinal intra- or interpatient differences. We currently employ the matlab based [http://www.nitrc.org/projects/fadtts/ FADTTS] analysis tool. A corresponding wrapper application for integration into DTIAtlasFiberAnalyzer is in progress.
 ==='''Merging statistics back to the original fiber bundle: '''===

@@ Line 27: / Line 27: @@
 [[Image:DTI_Atlas_Population_FiberTracts.png|thumb|right|300px|DTI atlas generation with fiber tracts]]
-==='''Unbiased DTI atlas building or atlas mapping: '''===
+==='''Unbiased DTI atlas building or atlas mapping via DTIAtlasBuilder: '''===
 Unbiased diffeomorphic atlas building is used to compute a normalized coordinate system for populations of diffusion images. The diffeomorphic transformations between each subject and the atlas provide spatial normalization for the comparison of tract statistics. This is achieved via the DTIAtlasBuilder tool.
 * '''DTI atlas creation:''' A DTI atlas can be generated for a specific study by averaging all individual subjects. For longitudinal studies, a [[Projects:LongitudinalAtlasBuilding|deformable longitudinal DTI atlas method]] can be used.