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Anatomical Accuracy of Standard-Practice Tractography Algorithms in the Motor System - A Histological Validation in the Squirrel Monkey Brain

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Anatomical Accuracy of Standard-Practice Tractography Algorithms in the Motor System - A Histological Validation in the Squirrel Monkey Brain

Kurt G Schilling et al. Magn Reson Imaging.

Abstract

For two decades diffusion fiber tractography has been used to probe both the spatial extent of white matter pathways and the region to region connectivity of the brain. In both cases, anatomical accuracy of tractography is critical for sound scientific conclusions. Here we assess and validate the algorithms and tractography implementations that have been most widely used - often because of ease of use, algorithm simplicity, or availability offered in open source software. Comparing forty tractography results to a ground truth defined by histological tracers in the primary motor cortex on the same squirrel monkey brains, we assess tract fidelity on the scale of voxels as well as over larger spatial domains or regional connectivity. No algorithms are successful in all metrics, and, in fact, some implementations fail to reconstruct large portions of pathways or identify major points of connectivity. The accuracy is most dependent on reconstruction method and tracking algorithm, as well as the seed region and how this region is utilized. We also note a tremendous variability in the results, even though the same MR images act as inputs to all algorithms. In addition, anatomical accuracy is significantly decreased at increased distances from the seed. An analysis of the spatial errors in tractography reveals that many techniques have trouble properly leaving the gray matter, and many only reveal connectivity to adjacent regions of interest. These results show that the most commonly implemented algorithms have several shortcomings and limitations, and choices in implementations lead to very different results. This study should provide guidance for algorithm choices based on study requirements for sensitivity, specificity, or the need to identify particular connections, and should serve as a heuristic for future developments in tractography.

Keywords: Accuracy; Diffusion magnetic resonance imaging; Dispersion; HARDI; Histology; Tractography; Validation.

Figures

Fig. 1.
Fig. 1.
Methodology pipeline. High resolution BDA micrographs are registered to the corresponding digital photograph of the frozen tissue block, which is registered to the 3D diffusion MRI volume. From the micrograph, BDA is automatically segmented, resulting in a BDA density map. From diffusion MRI, tractography is performed, resulting in tract density maps. Direct, voxel-by-voxel comparisons can now be made between histology and diffusion tractography.
Fig. 2.
Fig. 2.
Histological Results. BDA density is shown overlaid on the non-diffusion weighted volume for five coronal slices for monkey #1 (A) and monkey #2 (C). A BDA mask is shown as a volume rendering indicating the presence of BDA in a given voxel for monkeys #1 (B) and #2 (D). Injection region is shown in blue, BDA mask is shown in yellow.
Fig. 3.
Fig. 3.
Standard-practice pipelines vary widely in resulting tractography reconstructions. Diffusion Tractograms for streamline-generating algorithms are shown in both coronal and sagittal planes, for 10 randomly selected algorithms.
Fig. 4.
Fig. 4.
Voxel-wise anatomical accuracy measures. Values for overlap (A), overreach (B), modified Hausdorff distance [mean] (units of voxels) (C), and modified Hasudorff distance [90th percentile] (D) are shown for all algorithms. Reconstruction methods are designated by symbol shape, subject number by color, and tracking algorithm by the presence (or absence) of shape fill.
Fig. 5.
Fig. 5.
ROI-based anatomical accuracy measures. Values for sensitivity (A), specificity (B), and accuracy (C) are shown for each algorithm, along with ROC plots of sensitivity vs. 1 – specificity for animal #1 (D) and animal #2 (E). Shapes, color, and fill are the same as in Fig. 4.
Fig. 6.
Fig. 6.
Reconstruction strategy affects track anatomical accuracy measures. Algorithms were grouped by reconstruction strategy, and statistically significant differences are indicated by solid bars – results are shown for both monkeys.
Fig. 7.
Fig. 7.
Tracking algorithm affects track anatomical accuracy measures. Algorithms were grouped by tracking strategy (deterministic and probabilistic), and statistically significant differences are indicated by solid bars – results are shown for both monkeys.
Fig. 8.
Fig. 8.
Probabilistic threshold affects track anatomical accuracy measures. Analysis is performed on algorithm #7 and #8 for subject #1 (red) and #2 (blue) which differ only in seed (#8 uses a dilated seed). Vertical lines represent thresholds at 5%, 10%, 20%, and 50% of the maximum number of streamlines in a voxel.
Fig. 9.
Fig. 9.
Track length (distance from seed) affects track anatomical accuracy measures. Measures are binned across equidistant intervals (in mm) of < 4.9, 4.9–9.8, 9.8–14.7, 14.7–19.6, and > 19.6 – results are shown for both monkeys.
Fig. 10.
Fig. 10.
Algorithms vary in estimating both spatial extent and connectivity. The number of algorithms reaching given voxels (top) are shown overlaid on select coronal slices. On the scale of ROIs (bottom), if an algorithm has at least one streamline reach a region known to be occupied by BDA, the index displays as a color (as opposed to black for “no connection”). Colors indicate the reconstruction method used (red: DTI; green: Qball; blue: B&S; cyan: CSD). Regions on the vertical axis are listed in order of BDA densities derived from histology (i.e. most BDA occurs in M1).
Fig. 11.
Fig. 11.
Where tractography goes wrong. Density maps overlaid on the BDA fiber mask indicating where tractography first exits the BDA mask are shown for DTI (left), CSD (middle), and QBI (right), in three different orientations. Note that for the B&S algorithm, streamline outputs are not given, so we cannot query where error occurs. Results for subject #2 are given as Supplementary Fig. 3. Error density maps are scaled individually from maximum to minimum error (see colorbar) where gray indicates no streamline error.
Fig. 12.
Fig. 12.
Several sources contribute to tractography error. Pie plots show where error occurs (A) as well as BDA volume distribution in white and gray matter (B). The Euclidean distance to error voxels is shown for white and gray matter (C) as well as the distance to all voxels occupied by BDA (D). The streamline length to the error is shown for white and gray matter (E). Finally, the BDA density right before an error occurs is shown (F), as well as the overall BDA density distribution (G), and the percent of BDA density represented for each algorithm (H). Results for subject #2 are given as Supplementary Fig. 3.

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