BundleCleaner : Unsupervised Denoising and Subsampling of Diffusion MRI-Derived Tractography Data.

Autor: Feng Y; Imaging Genetics Center, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States., Chandio BQ; Imaging Genetics Center, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States., Villalón-Reina JE; Imaging Genetics Center, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States., Thomopoulos SI; Imaging Genetics Center, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States., Joshi H; Multimodal Brain Image Analysis Laboratory, Translational Psychiatry Laboratory, National Institute of Mental Health and Neuro Sciences, Bengaluru, Karnataka, India., Nair G; Multimodal Brain Image Analysis Laboratory, Translational Psychiatry Laboratory, National Institute of Mental Health and Neuro Sciences, Bengaluru, Karnataka, India., Joshi AA; Signal and Image Processing Institute, Ming Hseih dept of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States., Venkatasubramanian G; Multimodal Brain Image Analysis Laboratory, Translational Psychiatry Laboratory, National Institute of Mental Health and Neuro Sciences, Bengaluru, Karnataka, India., John JP; Multimodal Brain Image Analysis Laboratory, Translational Psychiatry Laboratory, National Institute of Mental Health and Neuro Sciences, Bengaluru, Karnataka, India., Thompson PM; Imaging Genetics Center, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States.
Jazyk: angličtina
Zdroj: BioRxiv : the preprint server for biology [bioRxiv] 2023 Aug 21. Date of Electronic Publication: 2023 Aug 21.
DOI: 10.1101/2023.08.19.553990
Abstrakt: 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.
Databáze: MEDLINE