The lack of “gold standards” in Diffusion Weighted Imaging (DWI) makes validation cumbersome. To tackle this task, studies use translational analysis where results in humans are benchmarked against findings in other species. Non-Human Primates (NHP) are particularly interesting for this, as their cytoarchitecture is closely related to humans. However, tools used for processing and analysis must be adapted and finely tuned to work well on NHP images. Here, we propose versaFlow, a modular pipeline implemented in Nextflow, designed for robustness and scalability. The pipeline is tailored to in vivo NHP DWI at any spatial resolution; it allows for maintainability and customization. Processes and workflows are implemented using cutting-edge and state-of-the-art Magnetic Resonance Imaging (MRI) processing technologies and diffusion modeling algorithms, namely Diffusion Tensor Imaging (DTI), Constrained Spherical Deconvolution (CSD), and DIstribution of Anisotropic MicrOstructural eNvironments in Diffusion-compartment imaging (DIAMOND). Using versaFlow, we provide an in-depth study of the variability of diffusion metrics computed on 32 subjects from 3 sites of the Primate Data Exchange (PRIME-DE), which contains anatomical T1-weighted (T1w) and T2-weighted (T2w) images, functional MRI (fMRI), and DWI of NHP brains. This dataset includes images acquired over a range of resolutions, using single and multi-shell gradient samplings, on multiple scanner vendors. We perform a reproducibility study of the processing of versaFlow using the Aix-Marseilles site's data, to ensure that our implementation has minimal impact on the variability observed in subsequent analyses. We report very high reproducibility for the majority of metrics; only gamma distribution parameters of DIAMOND display less reproducible behaviors, due to the absence of a mechanism to enforce a random number seed in the software we used. This should be taken into consideration when future applications are performed. We show that the PRIME-DE diffusion data exhibits a great level of variability, similar or greater than results obtained in human studies. Its usage should be done carefully to prevent instilling uncertainty in statistical analyses. This hints at a need for sufficient harmonization in acquisition protocols and for the development of robust algorithms capable of managing the variability induced in imaging due to differences in scanner models and/or vendors.

中文翻译：

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versaFlow：用于分辨率适应扩散 MRI 处理的多功能管道及其在研究 PRIME-DE 数据库变异性中的应用

弥散加权成像 (DWI) 缺乏“黄金标准”，使得验证变得很麻烦。为了解决这一任务，研究使用转化分析，将人类的结果与其他物种的发现进行比较。非人类灵长类动物（NHP）对此特别感兴趣，因为它们的细胞结构与人类密切相关。然而，用于处理和分析的工具必须经过调整和微调才能在 NHP 图像上正常工作。在这里，我们提出了VersaFlow，这是一种在 Nextflow 中实现的模块化管道，旨在实现稳健性和可扩展性。该管道专为体内NHP DWI 任意空间分辨率；它允许可维护性和定制。流程和工作流程是使用最先进的磁共振成像 (MRI) 处理技术和扩散建模算法来实现的，即扩散张量成像 (DTI)、约束球面解卷积 (CSD) 和各向异性微结构分布扩散室成像环境 (DIAMOND)。使用VersaFlow，我们对来自灵长类动物数据交换 (PRIME-DE) 3 个站点的 32 名受试者计算的扩散指标的变异性进行了深入研究，其中包含解剖 T1 加权 (T1w) 和 T2 加权 (T2w) NHP 大脑的图像、功能 MRI (fMRI) 和 DWI。该数据集包括在多个扫描仪供应商上使用单壳和多壳梯度采样在一系列分辨率下获取的图像。我们使用艾克斯-马赛站点的数据对VersaFlow 的处理进行了再现性研究，以确保我们的实施对后续分析中观察到的变异性的影响最小。我们报告大多数指标的重现性非常高；由于我们使用的软件中缺乏强制执行随机数种子的机制，因此只有 DIAMOND 的伽马分布参数显示出可重复性较差的行为。将来执行应用程序时应考虑到这一点。我们表明 PRIME-DE 扩散数据表现出很大程度的变异性，与人类研究中获得的结果相似或更大。应谨慎使用它，以防止在统计分析中注入不确定性。这暗示需要在采集协议中充分协调，并需要开发强大的算法，能够管理由于扫描仪型号和/或供应商的差异而引起的成像变化。