Tract probability maps in stereotaxic spaces: Analyses of white matter anatomy and tract-specific quantification

Abstract

Diffusion tensor imaging (DTI) is an exciting new MRI modality that can reveal detailed anatomy of the white matter. DTI also allows us to approximate the 3D trajectories of major white matter bundles. By combining the identified tract coordinates with various types of MR parameter maps, such as T₂ and diffusion properties, we can perform tract-specific analysis of these parameters. Unfortunately, 3D tract reconstruction is marred by noise, partial volume effects, and complicated axonal structures. Furthermore, changes in diffusion anisotropy under pathological conditions could alter the results of 3D tract reconstruction. In this study, we created a white matter parcellation atlas based on probabilistic maps of 11 major white matter tracts derived from the DTI data from 28 normal subjects. Using these probabilistic maps, automated tract-specific quantification of fractional anisotropy and mean diffusivity were performed. Excellent correlation was found between the automated and the individual tractography-based results. This tool allows efficient initial screening of the status of multiple white matter tracts.

Introduction

White matter diseases are often characterized by various types of MR abnormalities, including T₂-weighted hyperintensity, T₁-weighted hypointensity, reduced magnetization transfer ratio (MTR), and, more recently, by decreased diffusion anisotropy or increased diffusivity. For accurate correlation of anatomic abnormalities with neurologic dysfunction, precise characterization of lesion location is of great importance. However, localization of the lesion and quantification of its severity are challenging tasks. This is especially the case for the white matter, which often appears homogeneous in conventional MRI.

Diffusion tensor imaging (DTI) has the potential to improve the localization information of white matter lesions because it can reveal detailed anatomy of the white matter (Douek et al., 1991, Basser et al., 1994, Nakada and Matsuzawa, 1995, Pierpaoli et al., 1996, Makris et al., 1997, Pajevic and Pierpaoli, 1999, Stieltjes et al., 2001, Catani et al., 2002, Mori et al., 2002, Jellison et al., 2004, Wakana et al., 2004, Mori et al., 2005). Based on fiber orientation information obtained from DTI, we can identify the locations of various axonal bundles. By comparing this information with conventional MR parameter maps, we can identify specific white matter tracts that are affected by the lesions. We can extend this approach in a more systematic way by identifying the 3D trajectories of individual white matter tracts using 3D tract reconstruction or tractography (Conturo et al., 1999, Mori et al., 1999, Basser et al., 2000, Poupon et al., 2000, Parker et al., 2002). Once a tract of interest is defined in three dimensions, we can superimpose its coordinates on MR parameter maps to perform quantitative tract-specific monitoring of pathological conditions (Virta et al., 1999, Xue et al., 1999, Stieltjes et al., 2001, Glenn et al., 2003, Wilson et al., 2003, Partridge et al., 2004, Pagani et al., 2005).

It has been shown that tractography can faithfully reconstruct the cores of prominent white matter tracts by using existing anatomical knowledge as an anatomical constraint (Stieltjes et al., 2001, Catani et al., 2002, Mori et al., 2002, Huang et al., 2004, Jellison et al., 2004). However, the results are sensitive to noise, partial volume effects, and convolution of axonal structures with different orientations within a voxel. Furthermore, diseased brains often have altered DTI parameters that could affect the tractography results. For instance, even if a tract of interest has a normal size and 3D trajectory, tractography may fail to reveal its entire course in the presence of decreased diffusion anisotropy (Fig. 1).

In this paper, we describe probabilistic maps of white matter tracts using a DTI database of normal adult subjects. Tractography results were transformed from individuals into a template and group-averaged trajectories were calculated. The purpose is two-fold. First, the population-averaged statistical maps can define the standard coordinates of the reproducible regions (cores) of the tracts. The resultant statistical maps of the normal population can then be used as a reference for abnormal white matter anatomy in neurodegenerative diseases. Second, the statistical template can be applied to individual patient data for automated white matter parcellation and for tract-specific quantification of MR parameters. This would mean that tractography for each individual is no longer necessary and the examination of multiple tracts can be performed automatically. This approach effectively eliminates the necessity to establish tractography protocols (e.g., descriptions to reproducibly define locations and sizes of seeding pixels across subjects) and to measure the reproducibility of the protocol. Although the quality of this template-based brain parcellation is heavily influenced by registration quality, this type of template-based quantification should provide an efficient means of initial assessment of the white matter status.

We performed reconstruction of 11 major white matter tracts using DTI data from 28 normal subjects, based on strict tracking protocols (Wakana et al., 2005), and registered the coordinates into a common template in the DTI-JHU space (lbam.med.jhmi.edu or www.DtiStudio.org) (Wakana et al., 2004) and the MNI-ICBM152 space (www.loni.ucla.edu/ICBM) (Mazziotta et al., 2001). This paper explains the tracking and normalization procedures for our atlas and demonstrates how it can be used to perform lesion-tract correlation studies. The results with the automated approach are then compared with a non-automated method, in which tractography is performed for individual subjects for validation.