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.
The evaluation of analysis methods for diffusion tensor imaging (DTI) remains challenging due to the lack of
gold standards and validation frameworks. Significant work remains in developing metrics for comparing fiber
bundles generated from streamline tractography. We propose a set of volumetric and tract oriented measures
for evaluating tract differences. The different methodsdeveloped for this assessment work are: an overlap measurement,
a point cloud distance and a quantification of the diffusion properties at similar locations between
fiber bundles. The application of the measures in this paper is a comparison of atlas generated tractography
to tractography generated in individual images. For the validation we used a database of 37 subject DTIs, and
applied the measurements on five specific fiber bundles: uncinate, cingulum (left and right for both bundles) and
genu. Each measurments is interesting for specific use: the overlap measure presents a simple and comprehensive
metric but is sensitive to partial voluming and does not give consistent values depending on the bundle geometry.
The point cloud distance associated with a quantile interpretation of the distribution gives a good intuition of
how close and similar the bundles are. Finally, the functional difference is useful for a comparison of the diffusion
properties since it is the focus of many DTI analysis to compare scalar invariants. The comparison demonstrated
reasonable similarity of results. The tract difference measures are also applicable to comparison of tractography
algorithms, quality control, reproducibility studies, and other validation problems.
Diffusion Tensor Imaging (DTI) has become an important MRI procedure to investigate the integrity of white matter in
brain in vivo. DTI is estimated from a series of acquired Diffusion Weighted Imaging (DWI) volumes. 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. Currently, routine DTI QC
procedures are conducted manually by visually checking the DWI data set in a gradient by gradient and slice by slice
way. The results often suffer from low consistence across different data sets, lack of agreement of different experts, and
difficulty to judge motion artifacts by qualitative inspection. Additionally considerable manpower is needed for this step
due to the large number of images to QC, which is common for group comparison and longitudinal studies, especially
with increasing number of diffusion gradient directions. We present a framework for automatic DWI QC. We developed a tool called DTIPrep which pipelines the QC steps with a detailed protocoling and reporting facility. And it is fully open source. This framework/tool has been successfully applied to several DTI studies with several hundred DWIs in our lab as well as collaborating labs in Utah and Iowa. In our studies, the tool provides a crucial piece for robust DTI analysis in brain white matter study.
The segmentation of the subcortical structures of the brain is required for many forms of quantitative neuroanatomic
analysis. The volumetric and shape parameters of structures such as lateral ventricles, putamen,
caudate, hippocampus, pallidus and amygdala are employed to characterize a disease or its evolution. This paper
presents a fully automatic segmentation of these structures via a non-rigid registration of a probabilistic atlas
prior and alongside a comprehensive validation.
Our approach is based on an unbiased diffeomorphic atlas with probabilistic spatial priors built from a
training set of MR images with corresponding manual segmentations. The atlas building computes an average
image along with transformation fields mapping each training case to the average image. These transformation
fields are applied to the manually segmented structures of each case in order to obtain a probabilistic map
on the atlas. When applying the atlas for automatic structural segmentation, an MR image is first intensity
inhomogeneity corrected, skull stripped and intensity calibrated to the atlas. Then the atlas image is registered
to the image using an affine followed by a deformable registration matching the gray level intensity. Finally, the
registration transformation is applied to the probabilistic maps of each structures, which are then thresholded
at 0.5 probability.
Using manual segmentations for comparison, measures of volumetric differences show high correlation with
our results. Furthermore, the dice coefficient, which quantifies the volumetric overlap, is higher than 62% for all
structures and is close to 80% for basal ganglia. The intraclass correlation coefficient computed on these same
datasets shows a good inter-method correlation of the volumetric measurements. Using a dataset of a single
patient scanned 10 times on 5 different scanners, reliability is shown with a coefficient of variance of less than 2
percents over the whole dataset. Overall, these validation and reliability studies show that our method accurately
and reliably segments almost all structures. Only the hippocampus and amygdala segmentations exhibit relative
low correlation with the manual segmentation in at least one of the validation studies, whereas they still show
appropriate dice overlap coefficients.