Difference between revisions of "2011 Winter Project Week:longitudinal dti analysis"
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| − | Begin with utilizing the existing DTI registration resources for co-registering the images from individual subjects (at varying timepoints: intra-subject). The input images would be scalars derived from the DWI/DTI inputs. For the specific scenarios of TBI and HD, different algorithms/parameter settings for registration would be compared. The aim is to build an atlas using the transformed images and getting transformation fields back to each timepoint image. This would be applied to deform the tensor | + | Begin with utilizing the existing DTI registration resources for co-registering the images from individual subjects (at varying timepoints: intra-subject). The input images would be scalars derived from the DWI/DTI inputs. For the specific scenarios of TBI and HD, different algorithms/parameter settings for registration would be compared. The aim is to build an atlas using the transformed images and getting transformation fields back to each timepoint image. This would be applied to deform the tensor fields and finally come up with a DTI atlas. |
| − | Post successful DTI atlas building, we would proceed with tractography followed by arc-length parametrization along the fiber bundles and the use of existing DTI-statistical-analysis framework with along-tract kernel regression. The framework was originally developed by Casey Goodlett and has been modified and updated by the Utah and UNC groups. | + | Post successful DTI atlas building, we would proceed with tractography in the DTI atlas image, transformed back again to get the same tract geometry from individual images. This is followed by arc-length parametrization along the fiber bundles and the use of existing DTI-statistical-analysis framework with along-tract kernel regression. The framework was originally developed by Casey Goodlett and has been modified and updated by the Utah and UNC groups. |
Revision as of 13:36, 10 January 2011
Home < 2011 Winter Project Week:longitudinal dti analysis
Longitudinal DTI Analysis for patient follow-up images- complete processing pipeline
Key Partners
- Utah: Anuja Sharma, Guido Gerig
- UNC: Martin Styner (Core 1)
- Iowa: Hans Johnson (HD Project)
- UCLA: Jack Van Horn (TBI Project)
Objective
To work on longitudinal DTI data from Traumatic Brain Injury data sets and Huntington's disease datasets. The aim is to analyze changes in diffusion in individual patients' follow up images. In the process, explore the inventory of tools needed (existing within or outside Slicer/ITK) and challenges faced in achieving the same, focusing mainly on DTI registration.
Approach, Plan
Begin with utilizing the existing DTI registration resources for co-registering the images from individual subjects (at varying timepoints: intra-subject). The input images would be scalars derived from the DWI/DTI inputs. For the specific scenarios of TBI and HD, different algorithms/parameter settings for registration would be compared. The aim is to build an atlas using the transformed images and getting transformation fields back to each timepoint image. This would be applied to deform the tensor fields and finally come up with a DTI atlas.
Post successful DTI atlas building, we would proceed with tractography in the DTI atlas image, transformed back again to get the same tract geometry from individual images. This is followed by arc-length parametrization along the fiber bundles and the use of existing DTI-statistical-analysis framework with along-tract kernel regression. The framework was originally developed by Casey Goodlett and has been modified and updated by the Utah and UNC groups.
Progress
We are currently exploring the Group-wise registration tool (Golland et al.) available within the NAMIC kit to come up with a DTI Atlas. We would also explore the projects 'DTI Registration and Resampling wizard' and 'DTI registration/processing pipeline in Slicer3' for more ideas and ease of access to tools needed for DTI Registration within Slicer.
References
- Casey B. Goodlett, P. Thomas Fletcher, John H. Gilmore, Guido Gerig. Group Analysis of DTI Fiber Tract Statistics with Application to Neurodevelopment. NeuroImage 45 (1) Supp. 1, 2009. p. S133-S142
