Fiber Tract-Oriented Statistics for Quantitative Diffusion Tensor MRI Analysis

UNC-Utah NA-MIC framework for DTI fiber tract analysis

Diffusion tensor imaging has become an important modality in the field of neuroimaging to capture changes in micro-organization and to assess white matter integrity or development. While there exists a number of tractography toolsets, these usually lack tools for preprocessing or to analyze diffusion properties along the fiber tracts. Currently, the field is in critical need of a coherent end-to-end toolset for performing an along-fiber tract analysis, accessible to non-technical neuroimaging researchers. The UNC-Utah NA-MIC DTI framework represents a coherent, open source, end-to-end toolset for atlas fiber tract based DTI analysis encompassing DICOM data conversion, quality control, atlas building, fiber tractography, fiber parameterization, and statistical analysis of diffusion properties. Most steps utilize graphical user interfaces (GUI) to simplify interaction and provide an extensive DTI analysis framework for non-technical researchers/investigators. We illustrate the use of our framework on a small sample, cross sectional neuroimaging study of eight healthy 1-year-old children from the Infant Brain Imaging Study (IBIS) Network. In this limited test study, we illustrate the power of our method by quantifying the diffusion properties at 1 year of age on the genu and splenium fiber tracts.

ATLAS-BASED FIBER CLUSTERING FOR MULTI-SUBJECT ANALYSIS OF HIGH ANGULAR RESOLUTION DIFFUSION IMAGING TRACTOGRAPHY

High angular resolution diffusion imaging (HARDI) allows in vivo analysis of the white matter structure and connectivity. Based on orientation distribution functions (ODFs) that represent the directionality of water diffusion at each point in the brain, tractography methods can recover major axonal pathways. This enables tract-based analysis of fiber integrity and connectivity. For multi-subject comparisons, fibers may be clustered into bundles that are consistently found across subjects. To do this, we scanned 20 young adults with HARDI at 4 T. From the reconstructed ODFs, we performed whole-brain tractography with a novel Hough transform method. We then used measures of agreement between the extracted 3D curves and a co-registered probabilistic DTI atlas to select key pathways. Using median filtering and a shortest path graph search, we derived the maximum density path to compactly represent each tract in the population. With this tract-based method, we performed tract-based analysis of fractional anisotropy, and assessed how the chosen tractography algorithm influenced the results. The resulting method may expedite population-based statistical analysis of HARDI and DTI.

Improved tractography alignment using combined volumetric and surface registration

Previously we introduced an automated high-dimensional non-linear registration framework, CVS, that combines volumetric and surface-based alignment to achieve robust and accurate correspondence in both cortical and sub-cortical regions (Postelnicu et al., 2009). In this paper we show that using CVS to compute cross-subject alignment from anatomical images, then applying the previously computed alignment to diffusion weighted MRI images, outperforms state-of-the-art techniques for computing cross-subject alignment directly from the DWI data itself. Specifically, we show that CVS outperforms the alignment component of TBSS in terms of degree-of-alignment of manually labeled tract models for the uncinate fasciculus, the inferior longitudinal fasciculus and the corticospinal tract. In addition, we compare linear alignment using FLIRT based on either fractional anisotropy or anatomical volumes across-subjects, and find a comparable effect. Together these results imply a clear advantage to aligning anatomy as opposed to lower resolution DWI data even when the final goal is diffusion analysis.

Simultaneous consideration of spatial deformation and tensor orientation in diffusion tensor image registration using local fast marching patterns

Diffusion tensor imaging (DTI) plays increasingly important roles in surgical planning, neurological disease diagnosis, and follow-up studies in recent years. In order to compare the tractography obtained from different subjects or the same subject at different timepoints, a key step is to spatially align DTI images. Different from scalar or multi-channel image registration, tensor orientation should be considered in DTI registration. Several DTI registration methods have been proposed before, and some of them are based on first extracting the orientation-invariant features and then registering images using traditional scalar or multi-channel registration techniques followed by tensor reorientation. They essentially do not fully use the tensor information. Other methods such as the piece-wise affine transformation and the diffeomorphic non-linear registration algorithms use analytical gradients of the registration objective functions by considering the reorientation of tensor during the registration. However, only local tensor information such as voxel tensor similarity is utilized in these algorithms, which can be regarded as a counterpart of the traditional intensity similarity-based image registration in the DTI case. This paper proposes a novel DTI image registration algorithm, called fast marching-based simultaneous registration. It not only considers the orientation of tensors but also utilizes the neighborhood tensor information of each voxel, which is extracted from a local fast marching algorithm around voxels of interest. Compared to the voxel-wise tensor similarity-based registration, richer and more distinctive tensor features are used in this algorithm to better define correspondences between DTI images. Thus, more robust and accurate registration results can be obtained. In the experiments, comparative results using the real DTI data show the advantages of the proposed algorithm.

Early postnatal development of corpus callosum and corticospinal white matter assessed with quantitative tractography

BACKGROUND AND PURPOSE

The early postnatal period is perhaps the most dynamic phase of white matter development. We hypothesized that the early postnatal development of the corpus callosum and corticospinal tracts could be studied in unsedated healthy neonates by using novel approaches to diffusion tensor imaging (DTI) and quantitative tractography.

MATERIALS AND METHODS

Isotropic 2 x 2 x 2 mm(3) DTI and structural images were acquired from 47 healthy neonates. DTI and structural images were coregistered and fractional anisotropy (FA), mean diffusivity (MD), and normalized T1-weighted (T1W) and T2-weighted (T2W) signal intensities were determined in central midline and peripheral cortical regions of the white matter tracts of the genu and splenium of the corpus callosum and the central midbrain and peripheral cortical regions of the corticospinal tracts by using quantitative tractography.

RESULTS

We observed that central regions exhibited lower MD, higher FA values, higher T1W intensity, and lower T2W intensity than peripheral cortical regions. As expected, MD decreased, FA increased, and T2W signal intensity decreased with increasing age in the genu and corticospinal tract, whereas there was no significant change in T1W signal intensity. The central midline region of the splenium fiber tract has a unique pattern, with no change in MD, FA, or T2W signal intensity with age, suggesting different growth trajectory compared with the other tracts. FA seems to be more dependent on tract organization, whereas MD seems to be more sensitive to myelination.

CONCLUSIONS

Our novel approach may detect small regional differences and age-related changes in the corpus callosum and corticospinal white matter tracts in unsedated healthy neonates and may be used for future studies of pediatric brain disorders that affect developing white matter.

Diffusion tensor imaging and tractwise fractional anisotropy statistics: quantitative analysis in white matter pathology

Background

Information on anatomical connectivity in the brain by measurements of the diffusion of water in white matter tracts lead to quantification of local tract directionality and integrity.

Methods

The combination of connectivity mapping (fibre tracking, FT) with quantitative diffusion fractional anisotropy (FA) mapping resulted in the approach of results based on group-averaged data, named tractwise FA statistics (TFAS). The task of this study was to apply these methods to group-averaged data from different subjects to quantify differences between normal subjects and subjects with defined alterations of the corpus callosum (CC).

Results

TFAS exhibited a significant FA reduction especially in the CC, in agreement with region of interest (ROI)-based analyses.

Conclusion

In summary, the applicability of the TFAS approach to diffusion tensor imaging studies of normal and pathologically altered brains was demonstrated.

Anisotropy creases delineate white matter structure in diffusion tensor MRI

Current methods for extracting models of white matter architecture from diffusion tensor MRI are generally based on fiber tractography. For some purposes a compelling alternative may be found in analyzing the first and second derivatives of diffusion anisotropy. Anisotropy creases are ridges and valleys of locally extremal anisotropy, where the gradient of anisotropy is orthogonal to one or more eigenvectors of its Hessian. We propose that anisotropy creases provide a basis for extracting a skeleton of white matter pathways, in that ridges of anisotropy coincide with interiors of fiber tracts, and valleys of anisotropy coincide with the interfaces between adjacent but distinctly oriented tracts. We describe a crease extraction algorithm that generates high-quality polygonal models of crease surfaces, then demonstrate the method on a measured diffusion tensor dataset, and visualize the result in combination with tractography to confirm its anatomic relevance.

Improved correspondence for DTI population studies via unbiased atlas building

We present a method for automatically finding correspondence in Diffusion Tensor Imaging (DTI) from deformable registration to a common atlas. The registration jointly produces an average DTI atlas, which is unbiased with respect to the choice of a template image, along with diffeomorphic correspondence between each image. The registration image match metric uses a feature detector for thin fiber structures of white matter, and interpolation and averaging of diffusion tensors use the Riemannian symmetric space framework. The anatomically significant correspondence provides a basis for comparison of tensor features and fiber tract geometry in clinical studies and for building DTI population atlases.

Diffusion tensor imaging: Application to the study of the developing brain

OBJECTIVE

To provide an overview of diffusion tensor imaging (DTI) and its application to the study of white matter in the developing brain in both healthy and clinical samples.

METHOD

The development of DTI and its application to brain imaging of white matter tracts is discussed. Forty-eight studies using DTI to examine diffusion properties of the developing brain are reviewed in the context of the structural magnetic resonance imaging literature. Reports of how brain diffusion properties are affected in pediatric clinical samples and how they relate to cognitive and behavioral phenotypes are reviewed.

RESULTS

DTI has been used successfully to describe white matter development in pediatric samples. Changes in white matter diffusion properties are consistent across studies, with anisotropy increasing and overall diffusion decreasing with age. Diffusion measures in relevant white matter regions correlate with behavioral measures in healthy children and in clinical pediatric samples.

CONCLUSIONS

DTI is an important tool for providing a more detailed picture of developing white matter than can be obtained with conventional magnetic resonance imaging alone.

Diffeomorphic Matching of Diffusion Tensor Images

This paper proposes a method to match diffusion tensor magnetic resonance images (DT-MRI) through the large deformation diffeomorphic metric mapping of tensor fields on the image volume, resulting in optimizing for geodesics on the space of diffeomorphisms connecting two diffusion tensor images. A coarse to fine multi-resolution and multi-kernel-width scheme is detailed, to reduce both ambiguities and computation load. This is illustrated by numerical experiments on DT-MRI brain and images.