BundleCleaner: Unsupervised Denoising and Subsampling of Diffusion MRI-Derived Tractography Data.
BundleCleaner: Unsupervised Denoising and Subsampling of Diffusion MRI-Derived Tractography Data.
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BundleCleaner：扩散 MRI 衍生纤维束成像数据的无监督去噪和子采样。
DOI：
10.1101/2023.08.19.553990
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Thompson,PaulM
中科院分区：
文献类型：
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作者：
Feng,Yixue;Chandio,BramshQ;Villalón-Reina,JulioE;Thomopoulos,SophiaI;Joshi,Himanshu;Nair,Gauthami;Joshi,AnandA;Venkatasubramanian,Ganesan;John,JohnP;Thompson,PaulM
我们提出了BundleCleaner，一个无监督的多步骤框架，可以过滤，去噪和子采样来自基于扩散MRI的全脑纤维束。我们的方法同时考虑了全局束结构和局部流线特征。我们将BundleCleaner应用于使用概率纤维束成像从印度老年人的独立临床样本中的单壳扩散MRI数据生成的束，并且所产生的“清洁”束可以更好地与寰椎束对齐，减少过度延伸。在下游tractometry分析，我们表明，清洁束，表示与原始点集的不到20%，可以鲁棒本地化沿道之间的微结构差异32名健康对照组和34名参与者与阿尔茨海默氏病的年龄范围从55至84岁。我们的方法可以帮助减少内存负担，提高计算效率时，与纤维束成像数据，并显示大规模多站点纤维束测量的承诺。
We presentBundleCleaner, 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 applyBundleCleanerto 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.