Abstract
We present BundleCleaner, an unsupervised multi-step framework that can filter, denoise and subsample bundles derived from diffusion MRI-based whole-brain tractography. Our approach considers both the global bundle structure and local streamline-wise features. We apply BundleCleaner to bundles generated from single-shell diffusion MRI data in an independent clinical sample of older adults from India using probabilistic tractography and the resulting ‘cleaned’ bundles can better align with the atlas bundles with reduced overreach. In a downstream tractometry analysis, we show that the cleaned bundles, represented with less than 20% of the original set of points, can robustly localize along-tract microstructural differences between 32 healthy controls and 34 participants with Alzheimer’s disease ranging in age from 55 to 84 years old. Our approach can help reduce memory burden and improving computational efficiency when working with tractography data, and shows promise for large-scale multi-site tractometry.
This work was supported by the NIH grants RF1AG057892 and R01AG060610, the Department of Science and Technology, Govt. of India, grant nos. DST-SR/CSI/73/2011 (G); DST-SR/CSI/70/2011 (G); and DST/CSRI/2017/249 (G).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
FiberNeat implementation is available at https://github.com/BramshQamar/FiberNeat/tree/main.
References
Andersson, J.L., Skare, S., Ashburner, J.: How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage 20(2), 870–888 (2003). https://doi.org/10.1016/S1053-8119(03)00336-7
Andersson, J.L., Sotiropoulos, S.N.: An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage 125, 1063–1078 (2016). https://doi.org/10.1016/j.neuroimage.2015.10.019
Ashburner, J., Friston, K.J.: Voxel-based morphometry-the methods. Neuroimage 11(6), 805–821 (2000). https://doi.org/10.1006/nimg.2000.0582
Astolfi, P., et al.: Tractogram filtering of anatomically non-plausible fibers with geometric deep learning (2020). arXiv:2003.11013
Chandio, B.Q., et al.: Bundle analytics, a computational framework for investigating the shapes and profiles of brain pathways across populations. Sci. Rep. 10(1), 17149 (2020). https://doi.org/10.1038/s41598-020-74054-4
Chandio, B.Q., et al.: FiberNeat: unsupervised streamline clustering and white matter tract filtering in latent space. Preprint, Neuroscience (2021). https://doi.org/10.1101/2021.10.26.465991
Côté, M.A., et al.: Tractometer: towards validation of tractography pipelines. Med. Image Anal. 17(7), 844–857 (2013). https://doi.org/10.1016/j.media.2013.03.009
Garyfallidis, E., et al.: QuickBundles, a method for tractography simplification. Front. Neurosci. 6, 175 (2012). https://doi.org/10.3389/fnins.2012.00175
Garyfallidis, E., et al.: Dipy, a library for the analysis of diffusion MRI data. Front. Neuroinform. 8 (2014). https://doi.org/10.3389/fninf.2014.00008
Garyfallidis, E., et al.: Recognition of white matter bundles using local and global streamline-based registration and clustering. Neuroimage 170, 283–295 (2018). https://doi.org/10.1016/j.neuroimage.2017.07.015
Girard, G., Whittingstall, K., Deriche, R., Descoteaux, M.: Towards quantitative connectivity analysis: reducing tractography biases. Neuroimage 98, 266–278 (2014). https://doi.org/10.1016/j.neuroimage.2014.04.074
Hatton, S.N., et al.: White matter abnormalities across different epilepsy syndromes in adults: an ENIGMA-Epilepsy study. Brain 143(8), 2454–2473 (2020). https://doi.org/10.1093/brain/awaa200
Jbabdi, S., Johansen-Berg, H.: Tractography: where do we go from here? Brain Connectivity 1(3), 169–183 (2011). https://doi.org/10.1089/brain.2011.0033
Jenkinson, M., Beckmann, C.F., Behrens, T.E., Woolrich, M.W., Smith, S.M.: FSL. NeuroImage 62(2), 782–790 (2012). https://doi.org/10.1016/j.neuroimage.2011.09.015
Kellner, E., et al.: Gibbs-ringing artifact removal based on local subvoxel-shifts: Gibbs-ringing artifact removal. Magn. Reson. Med. 76(5), 1574–1581 (2016). https://doi.org/10.1002/mrm.26054
Koshiyama, D., et al.: White matter microstructural alterations across four major psychiatric disorders: mega-analysis study in 2937 individuals. Mol. Psychiatry 25(4), 883–895 (2020). https://doi.org/10.1038/s41380-019-0553-7
Legarreta, J.H., et al.: Filtering in tractography using autoencoders (FINTA). Med. Image Anal. 72, 102126 (2021). https://doi.org/10.1016/j.media.2021.102126
Manjón, J.V., et al.: Diffusion weighted image denoising using overcomplete local PCA. PLoS ONE 8(9), e73021 (2013). https://doi.org/10.1371/journal.pone.0073021
Neto Henriques, R.: Advanced methods for diffusion MRI data analysis and their application to the healthy ageing brain (2017). https://doi.org/10.17863/CAM.29356
O’Donnell, L.J., Westin, C.F.: An introduction to diffusion tensor image analysis. Neurosurg. Clinics North America 22(2), 185–196, viii (2011). https://doi.org/10.1016/j.nec.2010.12.004
Presseau, C., et al.: A new compression format for fiber tracking datasets. Neuroimage 109, 73–83 (2015). https://doi.org/10.1016/j.neuroimage.2014.12.058
Sarwar, T., et al.: Mapping connectomes with diffusion MRI: deterministic or probabilistic tractography? Magn. Reson. Med. 81(2), 1368–1384 (2019). https://doi.org/10.1002/mrm.27471
Savitzky, A., Golay, M.J.E.: Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36(8), 1627–1639 (1964). https://doi.org/10.1021/ac60214a047
Sharp, N., Crane, K.: A Laplacian for nonmanifold triangle meshes. Comput. Graph. Forum 39(5), 69–80 (2020). https://doi.org/10.1111/cgf.14069
Tournier, J.D., Calamante, F., Connelly, A.: Robust determination of the fibre orientation distribution in diffusion MRI. Neuroimage 35(4), 1459–1472 (2007). https://doi.org/10.1016/j.neuroimage.2007.02.016
Tournier, J.D., et al.: MRtrix3: a fast, flexible and open software framework for medical image processing and visualisation. Neuroimage 202, 116137 (2019). https://doi.org/10.1016/j.neuroimage.2019.116137
Wasserthal, J., Neher, P., Maier-Hein, K.H.: TractSeg - fast and accurate white matter tract segmentation. Neuroimage 183, 239–253 (2018). https://doi.org/10.1016/j.neuroimage.2018.07.070
Yeh, F.C., et al.: Population-averaged atlas of the macroscale human structural connectome and its network topology. Neuroimage 178, 57–68 (2018). https://doi.org/10.1016/j.neuroimage.2018.05.027
Zeng, J., et al.: 3D point cloud denoising using graph Laplacian regularization of a low dimensional manifold model (2019). arXiv:1803.07252 [cs]
Zhang, S., et al.: Hypergraph spectral analysis and processing in 3D point cloud. IEEE Trans. Image Process. 30, 1193–1206 (2021). https://doi.org/10.1109/TIP.2020.3042088
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Feng, Y. et al. (2023). BundleCleaner: Unsupervised Denoising and Subsampling of Diffusion MRI-Derived Tractography Data. In: Karaman, M., Mito, R., Powell, E., Rheault, F., Winzeck, S. (eds) Computational Diffusion MRI. CDMRI 2023. Lecture Notes in Computer Science, vol 14328. Springer, Cham. https://doi.org/10.1007/978-3-031-47292-3_14
Download citation
DOI: https://doi.org/10.1007/978-3-031-47292-3_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-47291-6
Online ISBN: 978-3-031-47292-3
eBook Packages: Computer ScienceComputer Science (R0)