MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust Quantitative Connectivity through Harmonized Preprocessing of Diffusion MRI.

ArXiv Pub Date : 2024-11-14
Nancy R Newlin, Kurt Schilling, Serge Koudoro, Bramsh Qamar Chandio, Praitayini Kanakaraj, Daniel Moyer, Claire E Kelly, Sila Genc, Jian Chen, Joseph Yuan-Mou Yang, Ye Wu, Yifei He, Jiawei Zhang, Qingrun Zeng, Fan Zhang, Nagesh Adluru, Vishwesh Nath, Sudhir Pathak, Walter Schneider, Anurag Gade, Yogesh Rathi, Tom Hendriks, Anna Vilanova, Maxime Chamberland, Tomasz Pieciak, Dominika Ciupek, Antonio Tristán Vega, Santiago Aja-Fernández, Maciej Malawski, Gani Ouedraogo, Julia Machnio, Christian Ewert, Paul M Thompson, Neda Jahanshad, Eleftherios Garyfallidis, Bennett A Landman
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Abstract

White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (DW-MRI) to qualitatively identify changes in white matter microstructure and connectivity. Yet, quantitative analysis of DW-MRI data is hindered by inconsistencies stemming from varying acquisition protocols. Specifically, there is a pressing need to harmonize the preprocessing of DW-MRI datasets to ensure the derivation of robust quantitative diffusion metrics across acquisitions. In the MICCAI-CDMRI 2023 QuantConn challenge, participants were provided raw data from the same individuals collected on the same scanner but with two different acquisitions and tasked with preprocessing the DW-MRI to minimize acquisition differences while retaining biological variation. Harmonized submissions are evaluated on the reproducibility and comparability of cross-acquisition bundle-wise microstructure measures, bundle shape features, and connectomics. The key innovations of the QuantConn challenge are that (1) we assess bundles and tractography in the context of harmonization for the first time, (2) we assess connectomics in the context of harmonization for the first time, and (3) we have 10x additional subjects over prior harmonization challenge, MUSHAC and 100x over SuperMUDI. We find that bundle surface area, fractional anisotropy, connectome assortativity, betweenness centrality, edge count, modularity, nodal strength, and participation coefficient measures are most biased by acquisition and that machine learning voxel-wise correction, RISH mapping, and NeSH methods effectively reduce these biases. In addition, microstructure measures AD, MD, RD, bundle length, connectome density, efficiency, and path length are least biased by these acquisition differences. A machine learning approach that learned voxel-wise cross-acquisition relationships was the most effective at harmonizing connectomic, microstructure, and macrostructure features, but requires the same subject be scanned at each site co-registered. NeSH, a spatial and angular resampling method, was also effective and has generalizable framework not reliant co-registration. Our code is available at https://github.com/nancynewlin-masi/QuantConn/.

MICCAI-CDMRI 2023 QuantConn 挑战赛 "通过对弥散核磁共振成像进行统一预处理实现强大的定量连接性 "的研究成果。
白质改变越来越多地与神经系统疾病及其进展有关。国际研究利用扩散加权磁共振成像(DW-MRI)来定性识别白质微观结构和连接性的变化。然而,DW-MRI 数据的定量分析却因不同的采集方案导致的不一致性而受到阻碍。目前迫切需要统一 DW-MRI 数据集的预处理,以确保在不同采集过程中得出可靠的定量弥散指标。在 MICCAI-CDMRI 2023 QuantConn 挑战赛中,参赛者获得了在同一台扫描仪上采集的同一人的原始数据,但采集方式不同,他们的任务是对 DW-MRI 进行预处理,以尽量减少采集差异,同时保留生物变异。参赛作品将根据跨采集束微结构测量、束形状特征和连接组学的可重复性和可比性进行评估。QuantConn 挑战赛的主要创新点在于:(1) 我们首次在协调的背景下评估束和束线图;(2) 我们首次在协调的背景下评估连接组学;(3) 我们比之前的协调挑战赛 MUSHAC 增加了 10 倍的受试者,比 SuperMUDI 增加了 100 倍。我们发现,束表面积、分数各向异性、连通组同质性、间度中心性、边缘数、模块性、结点强度和参与系数等测量指标受采集影响最大,而机器学习体素校正、RISH 映射和 NeSH 方法可有效减少这些偏差。此外,微观结构测量 AD、MD、RD、束长、连接体密度、效率和路径长度受采集差异的影响最小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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