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

被引：1

作者：

Feng, Yixue ^{[1
]}

Chandio, Bramsh Q. ^{[1
]}

Villalon-Reina, Julio E. ^{[1
]}

Thomopoulos, Sophia I. ^{[1
]}

Joshi, Himanshu ^{[3
]}

Nair, Gauthami ^{[3
]}

Joshi, Anand A. ^{[2
]}

Venkatasubramanian, Ganesan ^{[3
]}

John, John P. ^{[3
]}

Thompson, Paul M. ^{[1
]}

机构：

[1] Univ Southern Calif, Keck Sch Med, Imaging Genet Ctr, Marina Del Rey, CA 22203 USA

[2] Univ Southern Calif, Signal & Image Proc Inst, Ming Hseih Dept Elect & Comp Engn, Los Angeles, CA USA

[3] Natl Inst Mental Hlth & Neurosci, Multimodal Brain Image Anal Lab, Translat Psychiat Lab, Bengaluru, Karnataka, India

来源：

COMPUTATIONAL DIFFUSION MRI, CDMRI 2023 | 2023年 / 14328卷

关键词：

dMRI; tractography; denoising; tractometry;

D O I：

10.1007/978-3-031-47292-3_14

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：152 / 164

页数：13

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