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Automatically Identifying White Matter Tracts Using Cortical Labels

John A. Bogovic, Aaron Carass, Jing Wan, Bennett A. Landman, and Jerry L. Prince


Abstract
Introduction
Methods
Results
  Figures
  Tables
Conclusion
References

Abstract

Diffusion tensor imaging (DTI) has become a standard clinical procedure in assessing the health of white matter in the brain. Tractography, the tracing of individual fibers in the brain using DTI data, has begun to play a more central role in neuroscience research, particularly in understanding the relationships between brain connectivity and behavior. The measuring of features related to bundles of fibers, i.e., tracts or fasciculi, is currently problematic because of the need for manual interaction. This article presents an algorithm for the automatic identification of selected white matter tracts. It extracts fibers using the FACT algorithm and finds cortical gyral labels using a multi-atlas deformable registration scheme. Tracts are identified as the fibers passing between selected cortical labels. The quality of automatic labels are compared both visually and numerically against a well-accepted manual approach. The automatic approach is shown to be more consistent with conventional definitions of tracts and more repeatable on separate scans of the same subject.
  An illustration of the major white matter tracts.

Introduction

Diffusion tensor imaging (DTI) [1] is a recently developed imaging modality that provides insight into the direction of diffusion in tissue. By obtaining at least 6 diffusion-weighted images [2], a tensor that describes this diffusion can be computed. At each voxel, the direction of strongest diffusion is indicated by the principal eigenvector of the tensor. In cortical white matter (WM), it indicates the direction of parallel groups of myelinated axons, or fibers, which give rise to a high fractional anisotropy (FA) [3]. Fiber tracking algorithms [4, 5] have successfully been used to reconstruct white matter fibers in the human brain.

Methods

Acquisition

Scans for three subjects (2 Male, 1 Female, all right handed aged 23, 28 and 31) were obtained using a 3.0T Philips (Philips Medical Systems, Netherlands) Intera scanner. A single-shot EPI protocol with sensitivity encoding (SENSE) was used to obtain four separate 30 direction DTI acquisitions for each subject. The resulting images were 256x256x65 with a resolution of 0.828125x0.828125x2.2 mms. Additionally an MP-RAGE image was also obtained for each subject. The MP-RAGE was 256x256x130 with 0.828125x0.828125x1.1 mms resolution. All data was converted to have an isotropic voxel of length 0.828125mms, the resultant datasets were 256x256x173 in voxel dimension.
 

Geometry Correction

Echo Planar imaging (EPI) is a fast imaging sequence that is typically used in DTI in order to keep scan times as low as possible. EPI acquisitions suffer from geometric distortion due to susceptibility changes in the field of view (FOV). Since a structural MRI better represents a subject`s anatomy, we use it as our reference space. We accomplish this by registering the diffusion-weighted data to the structural acquisition. See Fig. 2 for an example.

Figure 2
(a) Distorted DWI acquisition
(b) Its outline on the MPRAGE
(c) Geometry-corrected DWI
(d) Its outline

Results

Diffusion tensor imaging (DTI) [1] is a recently developed imaging modality that provides insight into the direction of diffusion in tissue. By obtaining at least 6 diffusion-weighted images [2], a tensor that describes this diffusion can be computed. At each voxel, the direction of strongest diffusion is indicated by the principal eigenvector of the tensor. In cortical white matter (WM), it indicates the direction of parallel groups of myelinated axons, or fibers, which give rise to a high fractional anisotropy (FA) [3]. Fiber tracking algorithms [4, 5] have successfully been used to reconstruct white matter fibers in the human brain.
 

(a) (b)
(c) (d)
Fig. 1 Comparison of the left uncinate fasciculus obtained (a)-(b) manually, and (c)-(d) automatically,
with a transparent cortical surface
 
(a) (b)
(c) (d)
Fig. 2 Comparison of the left forceps-major obtained (a)-(b) manually, and (c)-(d) automatically,
with a transparent cortical surface
 
RightLeft
CNLOLICNLOLIFF
RightCN-007058
LO0-000660
LI00-2220234106
LeftCN1860234-000
LO1301300-00
LI59035400-0
FF000000-
Table 1 An example of anatomically incorrect gyral connectivity for a manually obtained (gray) F-MAJ and an anatomically correct and fuller result from the automatically obtained F-MAJ: Fiber counts for one data set are shown. CN: Cuneus, LO: Lateral Occipital, LI: Lingual and FF: Fusiform.
 
Suppose VM is the set of voxels containing manually generated fibers, and VA are those voxels containing automatically generated fibers. Then is the containment index (CI) of the volume of the manual tracts in the volume of the automatic tracts, the results of which are shown in Table 2. CI is a measure of how much of the manual tracts are recoverable with our automated approach.
 
SubjectF-MajF-MinUNC-RUNC-L
10.960.890.790.48
20.710.940.840.72
30.920.960.870.54
Table 2 Containment Index of manual tract in automatic tract for 4 major tracts over fiber volume for each subject averaged over the four DTI scans..
 

Conclusion

With this preliminary work on automated fasciculus identification we have shown that there are distinct flaws with using human raters for this task. While our approach is not yet perfect, it shows considerable promise. Our algorithm is currently limited by the resolution of the labeling scheme, which is why we currently recover more tracts then the anatomist expert. In future work, we will apply this method to more major fiber tracts and augment this new work with subcortical labels. We expect this will improve the quality of resulting tracts and facilitate the identification of different fasciculi.

References

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