Difference between revisions of "Clinically oriented TBI connectivity analysis in Slicer"
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Andrei Irimia, Bo Wang, Micah Chambers, Jack Van Horn, Marcel Prastawa, Guido Gerig | Andrei Irimia, Bo Wang, Micah Chambers, Jack Van Horn, Marcel Prastawa, Guido Gerig | ||
| − | + | Whereas our previously developed workflows for TBI longitudinal data analysis in 3D Slicer have been very useful for the analysis of severe TBI with large lesion loads, we have not yet investigated how robust our algorithms and methodologies are for the purpose of analyzing mild TBI data as well as data from TBI cases with low to moderate lesion loads. The purpose of this project is to work with our collaborators at the University of Utah and at Kitware in order to identify and solve the challenges posed by the application of TBI analysis algorithms to low- to moderate-load TBI cases. | |
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<div style="width: 27%; float: left; padding-right: 3%;"> | <div style="width: 27%; float: left; padding-right: 3%;"> | ||
<h3>Objective</h3> | <h3>Objective</h3> | ||
| − | + | * Develop 3D Slicer workflows dedicated to the analysis of multimodal MRI data from TBI cases with low to moderate lesion loads. | |
| + | * Identify optimal ways to use algorithms developed by the Utah and Kitware teams so that these algorithms can accommodate data sets which exhibit varying degrees of lesion extent | ||
| + | * Interact with our collaborators to determine how best to use their TBI pathology detection algorithms so that clinicians can use these methods with improved sensitivity and specificity with respect to the detection of lesions which are minor or otherwise difficult to detect via MRI | ||
</div> | </div> | ||
<div style="width: 27%; float: left; padding-right: 3%;"> | <div style="width: 27%; float: left; padding-right: 3%;"> | ||
<h3>Approach, Plan</h3> | <h3>Approach, Plan</h3> | ||
| − | + | * co-registration of DTI and MRI data will be performed in 3D Slicer to identify optimal workflows for data analysis | |
| + | * Multimodal MRI cases of TBI patients will be reviewed and analyzed with a focus on determining and improving the sensitivity threshold of pathology detection algorithms developed by our collaborators in Utah and North Carolina. | ||
| + | * DTI data will be analyzed to determine what connectivity changes occur longitudinally in the TBI brain | ||
| + | * the complex relationship between pathology-related hypo- and hyperintensities in MRI and connectomic abnormalities as revealed by DTI will be investigated and new methods will be developed for studying such relationships | ||
</div> | </div> | ||
<div style="width: 27%; float: left; padding-right: 3%;"> | <div style="width: 27%; float: left; padding-right: 3%;"> | ||
Revision as of 20:58, 12 December 2012
Home < Clinically oriented TBI connectivity analysis in SlicerKey Investigators
Andrei Irimia, Bo Wang, Micah Chambers, Jack Van Horn, Marcel Prastawa, Guido Gerig
Whereas our previously developed workflows for TBI longitudinal data analysis in 3D Slicer have been very useful for the analysis of severe TBI with large lesion loads, we have not yet investigated how robust our algorithms and methodologies are for the purpose of analyzing mild TBI data as well as data from TBI cases with low to moderate lesion loads. The purpose of this project is to work with our collaborators at the University of Utah and at Kitware in order to identify and solve the challenges posed by the application of TBI analysis algorithms to low- to moderate-load TBI cases.
Objective
- Develop 3D Slicer workflows dedicated to the analysis of multimodal MRI data from TBI cases with low to moderate lesion loads.
- Identify optimal ways to use algorithms developed by the Utah and Kitware teams so that these algorithms can accommodate data sets which exhibit varying degrees of lesion extent
- Interact with our collaborators to determine how best to use their TBI pathology detection algorithms so that clinicians can use these methods with improved sensitivity and specificity with respect to the detection of lesions which are minor or otherwise difficult to detect via MRI
Approach, Plan
- co-registration of DTI and MRI data will be performed in 3D Slicer to identify optimal workflows for data analysis
- Multimodal MRI cases of TBI patients will be reviewed and analyzed with a focus on determining and improving the sensitivity threshold of pathology detection algorithms developed by our collaborators in Utah and North Carolina.
- DTI data will be analyzed to determine what connectivity changes occur longitudinally in the TBI brain
- the complex relationship between pathology-related hypo- and hyperintensities in MRI and connectomic abnormalities as revealed by DTI will be investigated and new methods will be developed for studying such relationships
Progress
- Progress here
