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PREEMACS:猕猴大脑表面预处理和提取的流水线。

PREEMACS: Pipeline for preprocessing and extraction of the macaque brain surface.

机构信息

Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla. Blvd. Juriquilla, 3001 Querétaro, Querétaro, México.

Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla. Blvd. Juriquilla, 3001 Querétaro, Querétaro, México; International Institute of Information Technology, Hyderabad, India.

出版信息

Neuroimage. 2021 Feb 15;227:117671. doi: 10.1016/j.neuroimage.2020.117671. Epub 2020 Dec 24.

Abstract

Accurate extraction of the cortical brain surface is critical for cortical thickness estimation and a key element to perform multimodal imaging analysis, where different metrics are integrated and compared in a common space. While brain surface extraction has become widespread practice in human studies, several challenges unique to neuroimaging of non-human primates (NHP) have hindered its adoption for the study of macaques. Although, some of these difficulties can be addressed at the acquisition stage, several common artifacts can be minimized through image preprocessing. Likewise, there are several image analysis pipelines for human MRIs, but very few automated methods for extraction of cortical surfaces have been reported for NHPs and none have been tested on data from diverse sources. We present PREEMACS, a pipeline that standardizes the preprocessing of structural MRI images (T1- and T2-weighted) and carries out an automatic surface extraction of the macaque brain. Building upon and extending pre-existing tools, the first module performs volume orientation, image cropping, intensity non-uniformity correction, and volume averaging, before skull-stripping through a convolutional neural network. The second module performs quality control using an adaptation of MRIqc method to extract objective quality metrics that are then used to determine the likelihood of accurate brain surface estimation. The third and final module estimates the white matter (wm) and pial surfaces from the T1-weighted volume (T1w) using an NHP customized version of FreeSurfer aided by the T2-weighted volumes (T2w). To evaluate the generalizability of PREEMACS, we tested the pipeline using 57 T1w/T2w NHP volumes acquired at 11 different sites from the PRIME-DE public dataset. Results showed an accurate and robust automatic brain surface extraction from images that passed the quality control segment of our pipeline. This work offers a robust, efficient and generalizable pipeline for the automatic standardization of MRI surface analysis on NHP.

摘要

准确提取皮质脑表面对于皮质厚度估计至关重要,是进行多模态成像分析的关键要素,在该分析中,不同的指标在共同的空间中进行整合和比较。虽然脑表面提取在人类研究中已经得到广泛应用,但灵长类动物(NHP)神经影像学所特有的一些挑战阻碍了其在猕猴研究中的应用。尽管其中一些困难可以在采集阶段得到解决,但仍有一些常见的伪影可以通过图像预处理来最小化。同样,人类 MRI 有几个图像分析管道,但很少有用于 NHP 的自动皮质表面提取方法,也没有在来自不同来源的数据上进行测试。我们提出了 PREEMACS,这是一个用于标准化结构 MRI 图像(T1 和 T2 加权)预处理并自动提取猕猴大脑皮质表面的管道。该管道基于并扩展了现有的工具,第一个模块执行体积定向、图像裁剪、强度不均匀性校正和体积平均化,然后通过卷积神经网络进行颅骨剥离。第二个模块使用 MRIqc 方法的改编版进行质量控制,以提取客观质量指标,然后使用这些指标来确定准确的大脑表面估计的可能性。第三个也是最后一个模块使用 FreeSurfer 的 NHP 定制版本从 T1 加权体积(T1w)中提取白质(wm)和脑皮层表面,辅助使用 T2 加权体积(T2w)。为了评估 PREEMACS 的泛化能力,我们使用来自 PRIME-DE 公共数据集的 11 个不同站点采集的 57 个 T1w/T2w NHP 体积测试了该管道。结果表明,该管道在通过我们的管道的质量控制部分的图像中能够准确、稳健地自动提取大脑表面。这项工作为 NHP 上的 MRI 表面分析的自动标准化提供了一个稳健、高效和通用的管道。

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