Bundle-Specific Tractography

Bundle-Specific Tractography Francois Rheault, Etienne St-Onge, Jasmeen Sidhu, Quentin Chenot, Laurent Petit, and Maxime Descoteaux

Abstract Tractography allows the investigation of white matter fascicles. However, it requires a large amount of streamlines to be generated to cover the full spatial extent of desired bundles. In this work, a bundle-specific tractography algorithm was developed to increase reproducibility and sensitivity of white matter fascicle virtual dissection, thus avoiding the computation of a full brain tractography. Using fascicle priors from manually segmented bundles templates or atlases, we propose a novel local orientation enhancement methodology that overcomes reconstruction difficulties in crossing regions. To reduce unnecessary computation, tractography seeding and tracking were restricted to specific locales within the brain. These additions yield better spatial coverage, increasing the quality of the fanning in crossing regions, helping to accurately represent fascicle shape. In this work, tractography methods were analyzed and compared using a single bundle of interest, the corticospinal tract.

1 Introduction Tractography, a computational reconstruction of the white matter connections based on diffusion MRI (dMRI), is often used for structural connectivity analysis. Researchers often target a bundle of interest (BOI) within a whole brain tractogram, especially for purposes such as neurosurgical planning or assessing neurodegenera-

Francois Rheault and Etienne St-Onge contributed equally to this manuscript. F. Rheault · E. St-Onge () · J. Sidhu · M. Descoteaux Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Sherbrooke, QC, Canada e-mail: [email protected] Q. Chenot · L. Petit Groupe d’Imagerie Neurofonctionnelle, IMN, CNRS, CEA, Université de Bordeaux, Bordeaux, France © Springer International Publishing AG, part of Springer Nature 2018 E. Kaden et al. (eds.), Computational Diffusion MRI, Mathematics and Visualization, https://doi.org/10.1007/978-3-319-73839-0_10

129

130

F. Rheault et al.

tive diseases with large cohort analysis [1, 6, 9, 13]. In these cases, manual virtual dissection from a neuroanatomy expert is often used to extract the BOI based on well-known anatomical fascicle pathways once the tractogram is generated [4, 5]. Generally, the BOI is coarsely extracted and prefiltered, from the whole brain tractogram, using multiple regions of interest (ROI). Defined by a white matter (WM) and gray matter (GM) manual segmentation or atlas, i.e. FreeSurfer [11], ROI are used to define a pathway by a sequence of rules [23]. It is known that some bundles are more difficult to reconstruct than others, such as those traversing crossing regions [16, 21]. Furthermore, certain BOI have optimal tracking parameters to ensure adequate reconstruction [6, 19]. Additionally, instead of computing a whole brain tractogram, the efficiency of bundle-wise tracking algorithm is improved by seeding streamlines only in a known WM or GM ROI. As such, ROI seeding strategy is already used in multiple research projects to avoid generating unwanted streamlines [3, 7]. To overcome the fiber crossing difficulty, Chamberland et al. [7] also proposed a magnetic tracking (MAGNET) tool to manually enforce directions in strategic regions, improving the reconstruction of the desired bundle. Moreover, Dhollander et al. [12] recently suggested a method to sharpen orientations and increase spatial resolution based on streamlines. When targeting a specific bundle, this sharpening can be used to reduce the impact of the crossing problem. In this work, tractography is improved by incorporating bundle-specific priors, i.e. ROI and local orientations, based on a fascicle template or atlas. Tractography is subsequently guided accordingly using the orientation distribution priors extracted from the template streamlines at each voxel. As opposed to MAGNET, the proposed method does not rely upon manually placed regions to enforce a chosen direction. This automated approach uses information from the streamline template to enhance local modeling in a desired direction using a priori orientation distribution. It is important to note that compared to traditional tractography methods, this new bundle-specific tractography (BST) method increases the proportion of anatomically valid streamlines. In the context of our analysis, a valid streamline is one that respects the anatomical definition of the corticospinal tract (CST). The CST is a major WM fascicle that starts in the spinal cord, decussates, passes through various ROI delineated by neuroanatomy experts and terminates in the cortex. The details of the segmentation was left entirely to our neuroanatomist co-authors.

2 Methods 2.1 Template Dataset The template dataset comprises T1-weighted and diffusion-weighted images from 39 subjects of the BIL&GIN database [17]. The dMRI acquisition consists of 42

Bundle-Specific Tractography

131

gradient directions on a single shell (b D 1000 s/mm2 ) with a 2 mm isotropic resolution. At each voxel, the fiber orientation distribution (FOD) was computed using constrained spherical deconvolution (CSD) with spherical harmonics (order 6) [10, 20]. For each subject in this database, a whole brain tractogram was reconstructed using the probabilistic particle filtering tractography (PFT) with default parameters [15]. To capture streamline endpoints and pathways from a full brain tractography, the corticospinal tract (CST) template was manually dissected, using TrackVis [22], by neuroanatomy experts by positioning one cortical ROI and three WM ROI, one of which is close to the brainstem.

2.2 Template Construction To generate the template, all 39 T1 images were registered together in a common space using ANTS [2]. Subsequently, linear and non-linear warping were applied to the manually segmented left and right CST. The deformation of each dissected tractogram involved transforming each point (vertex) of each streamline. This CST template (Fig. 1a), made from multiple subjects aligned in a common space, results in a very dense and broad CST that fully covers the potential variability of the bundle and reduces the potential bias of our priors. This template was later employed to automatically mask and enhance FOD local orientation and thus help tractography, particularly in crossing regions.

Fig. 1 (a) Streamlines of the template (including all subjects), used as a prior. (b) Seeding (red) and tracking (white) masks, automatically extracted from the template

132

F. Rheault et al.

For each of the 39 subjects, CST of the 38 other subjects registered in the current subject space, were used as a template to generate maps and enhance FOD local orientations. This leave-one-out approach was used to maximize the number of examples to fully cover CST spatial extent without reducing the amount of data used for the validation.

2.3 Map Extraction CST-specific tractography requires the constructed CST template to be registered to each test subject native space. Once the CST is aligned to the subject, a tracking mask was automatically generated from voxels that intersect with streamlines from the template. A seeding mask was created in the same manner using the endpoints of streamlines. To reduce bias and incorporate the potential variability across subjects, the seeding and tracking regions were dilated through morphological operations. Tractography masks generated from the streamlines, present in the template, delineate a generous region where the CST should be (Fig. 1b). Since tractography is usually confined inside the WM, our prior seeding and tracking mask were restricted to the subject masks and maps [15].

2.4 Enhancing Orientations Guided tractography was achieved by computing, at every voxel, the local orientation histogram from nearby a priori streamlines. This template, based on a track orientation distribution (TOD) map, was used in various ways to enhance the FOD, as similarly done in [12]. Local directions observed in the CST template were used to enhance the FOD orientation and improve bundle-wise tracking. Formulated with a spherical harmonic delta function of order two, the TOD template creates a broad directional indication without imposing a strong prior [12]. During tractography, streamlines are generated by taking steps in a direction indicated/determined/estimated by the local FOD. The previous step direction combined with the local FOD values are used to weigh the probability of choosing a direction. Our approach uses the template TOD to modify the FOD weights according to the general a priori direction in the voxel. The term ‘lobe’ is often used to describe the probability distribution around a local maximum of the FOD. It is common that a streamline has difficulties going through a crossing, since slight changes in tracking direction quickly accumulate as streamlines follow the wrong lobe [21]. As shown in Fig. 2, at the crossing region of the arcuate fasciculus (AF), the corpus callosum (CC) and the corticospinal tract (CST), FOD have multiple lobes. In this work, the bundle of interest is the CST, meaning that the lobes associated with it must be enhanced (mainly in the z axis).


133

Fig. 2 Crossing region of the corpus callosum (CC) in red, the arcuate fasciculus (AF) in green and the corticospinal tract (CST) in blue. A coronal cross-section (left) along with 3D views in coronal, sagittal and axial (right)

Fig. 3 Orientation distribution of the crossing section presented in Fig. 2. The first row illustrates the crossing region (FA) and TOD maps generated from the different template (AF, CC and CST). In the second row, the first vignette shows the original dMRI FOD followed by bundle-enhanced FOD, a combination of the associated template TOD (from the first row) and the original FOD

In Fig. 3, the top row shows our template TOD map computed using the AF, CC and CST respectively, and the bottom row shows the enhanced FOD associated with each bundle. When a streamline reaches the crossing region, the probability of choosing an appropriate directional outcome increases. The prior amplifies the desired direction present in the TOD map, and thus, improves the directionality of

134

F. Rheault et al.

streamlines at the crossing. If the a priori bundle has a crossing or complex fanning, the TOD map correctly reflects the distribution and the appropriate weighting is achieved.

2.5 Experiments, Statistical Analysis and Metrics To evaluate the proposed enhancements, the same tractography algorithm and seeding regions were employed for each method: a probabilistic tractography initialized with five seeds per voxel given by the template endpoints. Utilizing Continuous Maps Criterion (CMC) [15], the tracking was done with default parameters without particle filtering. To distinguish the impact of each suggested improvement, i.e. the bundle-specific tracking mask and enhanced FOD, three tractography reconstructions were performed: 1. FOD with WM mask as a tracking region 2. FOD with our bundle-specific tracking mask 3. Enhanced FOD with our bundle-specific tracking mask. Each method was quantitatively evaluated by comparing percentages of valid streamlines and computational performance. The percentage of valid streamlines is the proportion of generated streamlines respecting the anatomical definition of a bundle using ROI drawn by the experts. The computational performance (efficiency) is represented by the total number of tractography iterations, i.e. tracking steps, leading to valid streamlines. For statistical analysis, left and right CST were analyzed separately and results were averaged together to highlight general trends. In addition to visual inspection, the following qualitative metrics were quantified in the template space to estimate the reconstruction quality: the bundle volume, average streamline length, and cross-subject dice with streamline density weighting [8].

3 Results Valid Streamlines and Bundle Volume Figures 4 and 5 compare a basic tracking method using the initial FOD and bundle-specific tracking mask to our approach using the enhanced FOD (CST-FOD) with the same bundle mask. An increase in both quality of the fanning and bundle coverage is observed with the BST enhanced CST-FOD when compared to classical tracking methods. The improved fanning with the BST, in Figs. 4 and 5, has quantitative impacts on the bundle volume (Table 1), as the fanning component of the CST represents the majority of its volume. This fanning occurs in the 3-way crossing region (Fig. 2), traditionally resulting in a small preponderance of streamlines reaching lateral portions of the primary motor and somatosensory cortices. The enhanced FOD, especially in the


135

Fig. 4 Comparison between a standard tracking (left) and the proposed bundle-specific tracking (right), where streamlines are colored with their local orientation (RGB)

Fig. 5 Resulting tractography on four random subjects (left to right): standard tracking in the first row and the proposed bundle-specific tracking in the second row

crossing region, improved the fanning of the CST toward cortex, which has a direct impact on the volume of the BOI. Table 1 shows the average results of each metric with all tracking strategies across every subject for both CST (left and right). The percentage of valid streamlines, after virtual dissection by our experts, is very low despite using strict seeding regions. As expected, using a bundle-specific tracking mask does not significantly improve the bundle shape reconstruction. Nevertheless, the proposed enhanced FOD and bundle-specific tractography (BST) noticeably increases the amount of valid streamlines, the bundle volume and the weighted dice. Computational Performance As opposed to the bundle reconstruction, integrating a bundle-specific tracking mask decreases the computation efficiency of tractography without reducing the percentage of valid streamlines. Thus, using a more restrictive mask that fully encompasses the BOI improves the efficiency. The use of enhanced corticospinal tract FOD (CST-FOD) increases the required time, but highly improves the efficiency with an increased number of valid streamlines.

136

F. Rheault et al.

Table 1 Quantitative results from our three experiments with corticospinal tracts (left and right) from all subjects (average ˙ std) Tracking % of valid orientation Mask streamlines Left corticospinal tract FOD WM 0.29% ˙ 0.18% FOD BS 0.72% ˙ 0.39% CST-FOD BS 4.43% ˙ 1.90% Right corticospinal tract FOD WM 0.14% ˙ 0.09% FOD BS 0.41% ˙ 0.23% CST-FOD BS 2.62% ˙ 1.29%

Bundle vol. (cm3 )

Weighted dice

Average length (cm)

Efficiency (%)

9.32 ˙ 3.24 0.62 ˙ 0.11 125.44 ˙ 3.84 1.30% ˙ 0.79% 11.47 ˙ 3.17 0.69 ˙ 0.10 124.00 ˙ 3.14 5.52% ˙ 2.82% 23.59 ˙ 5.00 0.85 ˙ 0.06 121.11 ˙ 3.04 19.19 %˙ 7.38% 6.25 ˙ 2.62 0.49 ˙ 0.12 120.15 ˙ 3.19 0.59% ˙ 0.36% 8.74 ˙ 2.87 0.60 ˙ 0.12 119.30 ˙ 2.64 3.04% ˙ 1.67% 21.02 ˙ 4.99 0.80 ˙ 0.07 115.52 ˙ 2.36 11.17% ˙ 4.98%

4 Discussion Valid Streamlines and Bundle Volume From our results (Figs. 4, 5 and Table 1), we observe that including orientation priors, with a bundle template, qualitatively and quantitatively improves the virtual dissection results by increasing the percentage of anatomically valid streamlines. This improvement comes from the enhanced CST-FOD in the crossing region (Fig. 2), allowing more streamlines to reach all ROI delineated by the neuroanatomy experts. The higher number of valid streamlines also improves spatial extent and volume of corticospinal tracts. Moreover, bundle reconstruction improvements, notably in shape and coverage, are crucial to help neuroanatomists find appropriate anatomical subject-specific ROI to fully dissect the desired BOI. It is important to note that only 20–25% of seeds result in a streamline returned by the probabilistic CMC tracking (see Girard et al.[14]). This proportion of streamlines is subsequently reduced, by the strict bundle segmentation, to our resulting percentage of valid streamlines (Table 1). Having a tractography method that helps to recover the full spatial extent of the BOI and correctly handles fanning in difficult regions is critical for neurosurgical intervention or pathological brains. Considering the strict virtual dissection of CST by the experts, the increased volume is caused by better spatial coverage, as the resulting bundle still respects the anatomical definition of the CST. Moreover, the proposed method produces a more reproducible CST reconstruction across subjects. This reduction in variability can be noted in the weighted dice metric, computed from the cross-subject bundle overlap in the common space. Computational Performance The main difference in computation efficiency is due to the prior imposed mask which immediately prevents any streamline from exiting the CST region, avoiding unnecessary tracking steps. This strategy reduces the number of tractography iterations and saves on time and computation without altering the resulting virtual dissection. This explains why Table 1 shows a major difference between using a standard mask and a bundle-specific mask. A slight increase in computation time is observed while using CST-FOD, because


137

streamlines follow the main direction of the CST instead of prematurely leaving the tracking mask without reaching ROI. From the efficiency metric, we observe that the time spent using the bundle-specific tracking results in a higher amount of valid streamlines. As an analogy to the efficiency measure, if we ran each tracking algorithm until we reached a predetermined number of valid streamlines, our BST approach would reach the desired number first. Future Improvements Since the proposed method uses local orientation priors to enhance FOD, other tracking algorithms could be used, but their potential pitfalls still remain. More advanced tracking methods use WM, GM and WM/GM interface maps to enforce anatomical priors [15, 18]. These recent methods could also benefit from local orientation enhancements from TOD during the reconstruction of the desired bundle. In addition, the creation and design of a WM atlas dedicated to this method would facilitate its use and guarantee the quality of the anatomical priors for well-known pathways. In this study, we examined the influence of our approach only on one bundle of interest. However, applying the same analysis to other difficult bundles, such as the anterior and posterior commissures, the fornix and the optic radiations could yield improved results and potential to compare various tractography methods. Since the BST performance relies on the quality of the registration and template construction, applying this method to unhealthy subjects might not be straightforward.

5 Conclusion We have developed a new template-based tractography method, with bundle-specific enhanced FOD, to overcome BOI reconstruction difficulties. Each step of the template construction is straightforward but its creation utilizing an a priori BOI, composed of streamlines, is a novel approach. This process greatly improves and accelerates tractography reconstruction of specific bundles along with facilitating virtual dissections. We have shown that our BST approach improves the spatial coverage of streamline endpoints and increases the quality of the fanning in crossing regions, while reducing computation time. This new bundle-specific tractography method could have a positive impact on the neuroscience community employing diffusion MRI to analyze specific WM fascicles.

References 1. Assaf, Y., Pasternak, O.: Diffusion tensor imaging (DTI)-based white matter mapping in brain research: a review. J. Mol. Neurosci. 34(1), 51–61 (2008). https://doi.org/10.1007/s12031-0070029-0 2. Avants, B., Epstein, C., Grossman, M., Gee, J.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26–41 (2008)

138

F. Rheault et al.

3. Behrens, T.E., Berg, H.J., Jbabdi, S., Rushworth, M., Woolrich, M.: Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? NeuroImage 34(1), 144–155 (2007) 4. Catani, M., De Schotten, M.T.: A diffusion tensor imaging tractography atlas for virtual in vivo dissections. Cortex 44(8), 1105–1132 (2008) 5. Catani, M., Howard, R.J., Pajevic, S., Jones, D.K.: Virtual in vivo interactive dissection of white matter fasciculi in the human brain. NeuroImage 17, 77–94 (2002) 6. Chamberland, M., Whittingstall, K., Fortin, D., Mathieu, D., Descoteaux, M.: Real-time multipeak tractography for instantaneous connectivity display. Front. Neuroinform. 8, 59 (2014) 7. Chamberland, M., Scherrer, B., Prabhu, S.P., Madsen, J., Fortin, D., Whittingstall, K., Descoteaux, M., Warfield, S.K.: Active delineation of Meyer’s loop using oriented priors through magnetic tractography (magnet). Hum. Brain Mapp. 38(1), 509–527 (2017) 8. Cousineau, M., Jodoin, P.M., Garyfallidis, E., Côté, M.A., Morency, F.C., Rozanski, V., Grand’Maison, M., Bedell, B.J., Descoteaux, M.: A test–retest study on parkinson’s ppmi dataset yields statistically significant white matter fascicles. NeuroImage: Clinical 16(Suppl. C), 222–233 (2017). http://www.sciencedirect.com/science/article/pii/S2213158217301869 9. Dayan, M., Monohan, E., Pandya, S., Kuceyeski, A., Nguyen, T.D., Raj, A., Gauthier, S.A.: Profilometry: a new statistical framework for the characterization of white matter pathways, with application to multiple sclerosis. Hum. Brain Mapp. 37(3), 989–1004 (2015). http:https:// doi.org/10.1002/hbm.23082 10. Descoteaux, M., Deriche, R., Knosche, T.R., Anwander, A.: Deterministic and probabilistic tractography based on complex fibre orientation distributions. IEEE Trans. Med. Imaging 28(2), 269–286 (2009) 11. Desikan, R.S., Ségonne, F., Fischl, B., Quinn, B.T., Dickerson, B.C., Blacker, D., Buckner, R.L., Dale, A.M., Maguire, R.P., Hyman, B.T., et al.: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31(3), 968–980 (2006) 12. Dhollander, T., Emsell, L., Hecke, W.V., Maes, F., Sunaert, S., Suetens, P.: Track orientation density imaging (TODI) and track orientation distribution (TOD) based tractography. NeuroImage 94, 312–336 (2014) 13. Dubois, J., Dehaene-Lambertz, G., Perrin, M., Mangin, J., Cointepas, Y., Duchesnay, E., Bihan, D.L., Hertz-Pannier, L.: Asynchrony of the early maturation of white matter bundles in healthy infants: quantitative landmarks revealed noninvasively by diffusion tensor imaging. Hum. Brain Mapp. 29(1), 14–27 (2008). https://doi.org/10.1002/hbm.20363 14. Girard, G., Descoteaux, M.: Anatomical tissue probability priors for tractography. In: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI’12)-Computational Diffusion MRI Workshop, pp. 174–185 (2012) 15. Girard, G., Descoteaux, M.: Towards quantitative connectivity analysis: reducing tractography biases. NeuroImage 98(1), 266–278 (2014) 16. Jbabdi, S., Johansen-Berg, H.: Tractography: where do we go from here? Brain connect. 1(3), 169–183 (2011) 17. Mazoyer, B., Mellet, E., Perchey, G., Zago, L., Crivello, F., Jobard, G., Delcroix, N., Vigneau, M., Leroux, G., Petit, L., Joliot, M., Tzourio-Mazoyer, N.: BIL&GIN: a neuroimaging, cognitive, behavioral, and genetic database for the study of human brain lateralization. NeuroImage 124 Part B, 1225–1231 (2016) 18. Smith, R.E., Tournier, J.D., Calamante, F., Connelly, A.: Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information. NeuroImage 62(3), 1924–1938 (2012) 19. Takemura, H., Caiafa, C.F., Wandell, B.A., Pestilli, F.: Ensemble tractography. PLOS Comput. Biol. 12(2), 1–22 (2016) 20. Tournier, J.D., Cho, K.H., Calamante, F., Yeh, C.H., Connelly, A., Lin, C.P.: Resolving crossing fibres using constrained spherical deconvolution: Validation using DWI phantom data. In: Proceedings of the International Society of Magnetic Resonance in Medicine, Berlin, p. 902 (2007)


139

21. Tournier, J.D., Calamante, F., Connelly, A.: MRtrix: diffusion tractography in crossing fiber regions. Int. J. Imaging Syst. Technol. 22(1), 53–66 (2012) 22. Wang, R., Benner, T., Sorensen, A., Wedeen., V.: Diffusion toolkit: a software package for diffusion imaging data processing and tractography. In: International Symposium on Magnetic Resonance in Medicine (ISMRM’07), p. 3720 (2007) 23. Wassermann, D., Makris, N., Rathi, Y., Shenton, M., Kikinis, R., Kubicki, M., Westin, C.F.: The white matter query language: a novel approach for describing human white matter anatomy. Brain Struct. Funct. 221(9), 4705–4721 (2016)