Diffusion Tensor Imaging (DTI) is currently the state of the art method for characterizing the microscopic tissue
structure of white matter in normal or diseased brain in vivo. DTI is estimated from a series of Diffusion Weighted
Imaging (DWI) volumes. DWIs suffer from a number of artifacts which mandate stringent Quality Control (QC)
schemes to eliminate lower quality images for optimal tensor estimation. Conventionally, QC procedures exclude
artifact-affected DWIs from subsequent computations leading to a cleaned, reduced set of DWIs, called DWI-QC.
Often, a rejection threshold is heuristically/empirically chosen above which the entire DWI-QC data is rendered
unacceptable and thus no DTI is computed. In this work, we have devised a more sophisticated, Monte-Carlo (MC)
simulation based method for the assessment of resulting tensor properties. This allows for a consistent, error-based
threshold definition in order to reject/accept the DWI-QC data. Specifically, we propose the estimation of two error
metrics related to directional distribution bias of Fractional Anisotropy (FA) and the Principal Direction (PD). The bias is
modeled from the DWI-QC gradient information and a Rician noise model incorporating the loss of signal due to the
DWI exclusions. Our simulations further show that the estimated bias can be substantially different with respect to
magnitude and directional distribution depending on the degree of spatial clustering of the excluded DWIs. Thus,
determination of diffusion properties with minimal error requires an evenly distributed sampling of the gradient
directions before and after QC.
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.