Projects:AtlasBasedDTIFiberAnalyzerFramework
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Atlas Based DTI FIber Analyzer Framework
This project aims to define an automatic framework for statistical comparison of fiber bundle diffusion properties between populations of diffusion weighted images.
Description
The general framework entails the following steps:
DWI and DTI quality control: 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.
Unbiased DTI atlas building or atlas mapping
- Unbiased DTI atlas building
- Mapping of an existing DTI atlas
Tractography within 3D Slicer
- 3D Slicer modules: Label seeding and ROI select
- Tractography with unscented kalman filter
Fiber cleanup/clustering:
FiberViewerLight enables several clustering methods: Length, Gravity, Hausdorff, and Mean methods but also a Normalized Cut algorithm.
DTIAtlasFiberAnalyzer
Statistical analysis performed by statistician
Merging statistics back to the original fiber bundle MergeStatWithFiber - an application part of DTI Fiber Tracts Statistics package-, allows population statistical information to be merged back to the atlas fiber bundle.
3D visualization within 3D Slicer Statically significan group differences can directly be displayed on a fiber bundle in 3D Slicer.
Publications
Key Investigators
- UNC Algorithms: Jean-Baptiste Berger, Benjamin Yvernault, Clement Vachet, Yundi Shi, Aditya Gupta, Martin Styner
- Utah Algorithms: Anuja Sharma, Sylvain Gouttard, Guido Gerig
