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Automated Diffusion Tensor Tractography

Posted Apr 30, 2012 Updated Apr 10, 2023
By Paolo G.P. Nucifora MD PhD , Xiaoying Wu MS , Elias R. Melhem MD PhD , Raquel E. Gur MD PhD , Ruben C. Gur PhD , Ragini Verma PhD
9 min read

Rationale and Objectives

Diffusion tensor tractography offers a unique perspective of white matter anatomy, but proper delineation of white matter tracts of interest generally requires the active involvement of an expert neuroanatomist. The investigators describe the implementation of an automated tractographic method requiring no user input and compare its results to those from user-driven tractography.

Materials and Methods

Fourteen healthy volunteers underwent diffusion tensor imaging at 3 T. Images were registered to a standard template, and predefined seed regions containing tract termini were transformed into subject space for use in unsupervised probabilistic tractography. The output was compared to the results of user-driven tractography performed on the same subjects.

Results

After the selection of suitable smoothing kernels and thresholds, the results of automated tractography closely approximated those of user-driven tractography. The main bodies of the cingulum, inferior fronto-occipital fasciculus, and inferior longitudinal fasciculus were depicted equally well by both methods. Discrepancies mainly arose at the periphery of these tracts, where anatomic uncertainty tends to be greatest.

Conclusions

Automated tractography can be used to depict white matter anatomy without need for user intervention, particularly if the main body of the tract is of greatest interest.

Diffusion tensor tractography is a recently developed imaging modality that can demonstrate the anatomy of white matter tracts in vivo . It is based on the observation that the diffusion of water in the brain occurs preferentially in directions parallel to axon bundles, which can be measured with an appropriate set of directionally encoded diffusion-weighted images . Because axons extend over multiple voxels, white matter tracts can be parceled as regions of directionally coherent diffusion tensors.

Tractography has found growing use in several clinical settings. However, several factors have constrained the use of tractography in routine practice. Among these is the operator dependence of its output. Typically, tractographic results are produced through an interactive process that relies on an experienced user to identify a “seed,” a likely location of each white matter tract. Tractography fibers are then inspected by the user and rejected if they do not conform to known white matter tract anatomy. Obtaining accurate and reproducible depictions of white matter can be time consuming, and user-introduced bias may complicate a population-based analysis . Semiautomated methods have been proposed to reduce operator dependence by using information in the neighborhood of a minimal seed region to improve seed reproducibility .

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Methods

Image Acquisition

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User-driven Tractography

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Automated Tractography

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Figure 1, Summary of method used to obtained automated tractographic results. Diffusion tensor imaging data were registered to a standard atlas to import predefined labels into subject space. The labels were converted to seed regions located at the termini of white matter tracts of interest. Unsupervised probabilistic tractography was used to generate probability maps of where each tract was likely to exist.

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Comparison of Tractographic Methods

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Results

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Figure 2, Tractography of the cingulum ( top ), inferior fronto-occipital fasciculus ( middle ), and inferior longitudinal fasciculus ( bottom ) of one of the subjects in the training set. User-driven tractographic fibers ( left ) are compared to maximum intensity projections of automated tractographic results ( right ). The automated tractographic results are color coded to indicate their probability values. Tractographic results are superimposed on b = 0 s 2mm diffusion-weighted images.

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Figure 3, Jaccard similarity coefficients between automated and user-driven tractography using various postprocessing criteria. Twelve automated tractographic maps contributed from four randomly selected subjects were used to improve the consistency between these two methods. Automated tractographic maps were subjected to Gaussian smoothing kernels of varying sizes and subjected to a range of thresholds. User-driven tractographic results were held as a reference. The maximal Jaccard coefficient was achieved with a smoothing kernel of 3 mm and a threshold of 0.02.

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Figure 4, Qualitative comparison of automated tractography to user-driven tractography. Maximum intensity projections of the cingulum ( top ), inferior fronto-occipital fasciculus ( middle ), and inferior longitudinal fasciculus ( bottom ) are shown from a representative subject. Voxels recognized by both methods as belonging to the tract of interest are shown in red. Voxels recognized only by user-driven tractography are shown in green. Voxels recognized only by automated tractography are shown in blue. For all three white matter tracts, the main body of the tract was recognized by both methods.

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Discussion

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Conclusions

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Academic Radiology, Volume 19, Issue 5
Journals General Radiology
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