Purpose: The UNC-Utah NA-MIC DTI framework represents a coherent, open source, atlas fiber tract based DTI
analysis framework that addresses the lack of a standardized fiber tract based DTI analysis workflow in the field. Most
steps utilize graphical user interfaces (GUI) to simplify interaction and provide an extensive DTI analysis framework for
non-technical researchers/investigators. Data: We illustrate the use of our framework on a 54 directional DWI
neuroimaging study contrasting 15 Smokers and 14 Controls. Method(s): At the heart of the framework is a set of tools anchored around the multi-purpose image analysis platform 3D-Slicer. Several workflow steps are handled via external modules called from Slicer in order to provide an integrated approach. Our workflow starts with conversion from DICOM, followed by thorough automatic and interactive quality control (QC), which is a must for a good DTI study. Our framework is centered around a DTI atlas that is either provided as a template or computed directly as an unbiased average atlas from the study data via deformable atlas building. Fiber tracts are defined via interactive tractography and clustering on that atlas. DTI fiber profiles are extracted automatically using the atlas mapping information. These tract parameter profiles are then analyzed using our statistics toolbox (FADTTS). The statistical results are then mapped back on to the fiber bundles and visualized with 3D Slicer. Results: This framework provides a coherent set of tools for DTI quality control and analysis. Conclusions: This framework will provide the field with a uniform process for DTI quality control and analysis.
This paper presents a novel pipeline for the registration of diffusion tensor images (DTI) with large pathological
variations to normal controls based on the use of a novel feature map derived from white matter (WM) fiber
tracts. The research presented aims towards an atlas based DTI analysis of subjects with considerable brain
pathologies such as tumors or hydrocephalus. In this paper, we propose a novel feature map that is robust against
variations in WM fiber tract integrity and use these feature maps to determine a landmark correspondence using
a 3D point correspondence algorithm. This correspondence drives a deformation field computed using Gaussian
radial basis functions(RBF). This field is employed as an initialization to a standard deformable registration
method like demons. We present early preliminary results on the registration of a normal control dataset to a
dataset with abnormally enlarged lateral ventricles affected by fatal demyelinating Krabbe disease. The results
are analyzed based on a regional tensor matching criterion and a visual assessment of overlap of major WM fiber
tracts. While further evaluation and improvements are necessary, the results presented in this paper highlight
the potential of our method in handling registration of subjects with severe WM pathology.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.