Tract-profiling and bundle statistics: a test-retest validation study

Martin Cousineau^{1}, Eleftherios Garyfallidis^{1}, Marc-Alexandre Côté^{1}, Pierre-Marc Jodoin^{1}, and Maxime Descoteaux^{1}

Diffusion-weighted images were acquired on 11 healthy subjects with 3 different acquisitions each along 64 uniformly distributed directions using a b-value of 1000 s/mm^{2} with 2 mm isotropic resolution.

Our processing pipeline is illustrated in Figure 1. Fiber ODFs and DTI/HARDI metrics are first extracted from the raw dMRI data using Dipy (a)^{5} (only FA will be reported in this abstract). Then, whole brain fODF tractography is performed using particle-filter tracking with anatomical priors (b).^{6} Next, 30 white matter bundles (c) are automatically dissected from the whole brain tractogram with *TractQuerier*.^{7} From this point on, each bundle is processed independently using our tractometry pipeline (d). First, short and long streamlines are pruned-out based on user-defined bundle dependent thresholds. Second, spurious streamlines (outliers) are removed with hierarchical QuickBundles (e).^{8} Third, centroids are computed as a mean streamline of the bundle using the minimum-distance-flipped metric (f).^{9} This centroid is subsampled on N=20 equidistant points and every point of every streamline of the bundle is assigned to the closest centroid point (g). With these assignments, it is possible to extract tract profiles for every metric of interest as well as a single average metric for each bundle of interest (h).

Our first experiment was to validate the shape and volume of our bundles. We first used registration to put all bundles in the same space to make sure they overlapped. Linear and nonlinear diffeomorphic registration was performed on the T1s via ANTs registration to the MNI 2009 template.^{10,11} The resulting warps were then applied to the corresponding bundles with nearest-neighbor interpolation. Once these volumes were in the same space, Dice’s coefficient, which measures the overlap of two volumes across intrasubject and intersubject acquisitions, was computed.^{12}

Afterwards, we tested the reproducibility of the metrics. By simply looking at the average value of a metric for an entire bundle, we computed a percentage difference for all possible pairs of acquisitions and checked whether intrasubject acquisitions differed from intersubject acquisitions.

Finally, by looking at the tract profiles, we computed the metric values of all 20 parts across all bundles for each acquisition and the variability with respect to the position in the bundle.

Figure 2 illustrates the average Dice’s coefficient for every bundle. We see that bundle volumes are about 10% closer when compared to the same subject than to other subjects, while still having a decent overlap overall.

We also provide a 33x33 similarity matrix (c.f. Fig. 3) in which each entry [i,j] contains the mean FA difference between acquisition i and j. The three intrasubject acquisitions being next to each other, we can clearly see that they are much closer together (in blue) than to others (in red). The maximal difference is only 9% since we are averaging the FA over the whole bundles.

Figure 4 shows the distribution of FA values along the tract profile of the left corticospinal tract (CST) for a single acquisition, and an average over all 33 acquisitions. We can clearly see that the FA values of the single subject are well within the standard deviation of all acquisitions. This observation also holds true for every other bundles.

1. Bells, S., Cercignani, M., Deoni, S., Assaf, Y., Pasternak, O., Evans, C. J., Leemans, A., and Jones, D. K. "Tractometry–comprehensive multi-modal quantitative assessment of white matter along specific tracts." In proc. of ISMRM, vol. 19 (2011): 678.

2. Ashburner, J., and Friston, K. J. "Voxel-based morphometry—the methods." NeuroImage, vol. 11, no. 6 (2000): 805-821.

3. Smith, S. M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T. E., Mackay, C. E., Watkins, K. E. et al. "Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data." NeuroImage, vol. 31, no. 4 (2006): 1487-1505.

4. Yeatman, J. D., Dougherty, R. F., Myall, N. J., Wandell, B. A., and Feldman, H. M. "Tract profiles of white matter properties: automating fiber-tract quantification." PLoS One, vol. 7, no. 11 (2012): e49790.

5. Garyfallidis, E., Brett, M., Amirbekian, B., Rokem, A., Van Der Walt, S., Descoteaux, M., Nimmo-Smith, I., and Dipy Contributors. "Dipy, a library for the analysis of diffusion MRI data." Frontiers in Neuroinformatics, vol. 8 (2014): 8.

6. Girard, G., Whittingstall, K., Deriche, R., and Descoteaux, M. "Towards quantitative connectivity analysis: reducing tractography biases." NeuroImage, vol. 98 (2014): 266-278.

7. Wassermann, D., Makris, N., Rathi, Y., Shenton, M., Kikinis, R., Kubicki, M., and Westin, C. F. "On describing human white matter anatomy: the white matter query language." In proc. of MICCAI, vol. 8149 (2013): 647-654.

8. Côté, M. A., Garyfallidis, E., Larochelle, H., and Descoteaux, M. "Cleaning up the mess: tractography outlier removal using hierarchical QuickBundles clustering." In proc. of ISMRM, vol. 23 (2015): 2844.

9. Garyfallidis, E., Brett, M., Correia, M. M., Williams, G. B., and Nimmo-Smith, I. "Quickbundles, a method for tractography simplification." Frontiers in Neuroscience, vol. 6 (2012): 175.

10. Avants, B. B., Epstein, C. L., Grossman, M., and Gee, J. C. "Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain." Medical Image Analysis, vol. 12, no. 1 (2008): 26-41.

11. Fonov, V. S., Evans, A. C., McKinstry, R. C., Almli, C. R., and Collins, D. L. "Unbiased nonlinear average age-appropriate brain templates from birth to adulthood." NeuroImage, vol. 47 (2009): S102.

12. Dice, L. R. "Measures of the amount of ecologic association between species." Ecology, vol. 26, no. 3 (1945): 297-302.

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)

3436