Automated processing pipeline for neonatal diffusion MRI in the developing Human Connectome Project.
Automated processing pipeline for neonatal diffusion MRI in the developing Human Connectome Project.
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DOI：
10.1016/j.neuroimage.2018.05.064
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发表时间：
2019-01-15
期刊：
影响因子：
5.7
通讯作者：
Sotiropoulos SN
中科院分区：
文献类型：
--
作者：
Bastiani M;Andersson JLR;Cordero-Grande L;Murgasova M;Hutter J;Price AN;Makropoulos A;Fitzgibbon SP;Hughes E;Rueckert D;Victor S;Rutherford M;Edwards AD;Smith SM;Tournier JD;Hajnal JV;Jbabdi S;Sotiropoulos SN
正在开发的人类连接组项目将创建并向科学界提供月经后20至44周的功能和结构大脑连接的四维地图，以探索遗传和环境对大脑发育的影响，以及连接和神经认知功能之间的关系。目前正在采集来自胎儿和新生儿的大量多模态MRI数据，沿着遗传、临床和发育信息。在本概述中，我们描述了新生儿弥散MRI（dMRI）图像处理管道和该项目的结构连接方面。新生儿dMRI数据提出了具体的挑战，用于成人数据的标准分析技术并不直接适用。我们开发了一个处理管道，直接处理新生儿特定的问题，例如严重的运动和运动相关的伪影、大脑尺寸小、脑含水量高和各向异性降低。该管道允许自动分析体内dMRI数据，探测组织微观结构，重建许多主要的白色物质束，并包括识别处理问题或不一致性的自动质量控制框架。我们在这里描述的管道，并提出了一个范例分析的数据，从140名婴儿在月经后38-44周的年龄成像。一个全面的自动化管道，用于一致地分析新生儿dMRI数据。优化的运动和失真校正，以解决新生儿的具体挑战。自动化QC框架允许检测问题并量化数据质量。自动化的白色物质分割允许提取特定的道掩模。对140名婴儿在月经后38-44周成像的初步数据分析。
The developing Human Connectome Project is set to create and make available to the scientific community a 4-dimensional map of functional and structural cerebral connectivity from 20 to 44 weeks post-menstrual age, to allow exploration of the genetic and environmental influences on brain development, and the relation between connectivity and neurocognitive function. A large set of multi-modal MRI data from fetuses and newborn infants is currently being acquired, along with genetic, clinical and developmental information. In this overview, we describe the neonatal diffusion MRI (dMRI) image processing pipeline and the structural connectivity aspect of the project. Neonatal dMRI data poses specific challenges, and standard analysis techniques used for adult data are not directly applicable. We have developed a processing pipeline that deals directly with neonatal-specific issues, such as severe motion and motion-related artefacts, small brain sizes, high brain water content and reduced anisotropy. This pipeline allows automated analysis of in-vivo dMRI data, probes tissue microstructure, reconstructs a number of major white matter tracts, and includes an automated quality control framework that identifies processing issues or inconsistencies. We here describe the pipeline and present an exemplar analysis of data from 140 infants imaged at 38–44 weeks post-menstrual age. A comprehensive and automated pipeline to consistently analyse neonatal dMRI data. Optimised motion and distortions correction to address newborn specific challenges. The automated QC framework allows to detect issues and to quantify data quality. Automated white matter segmentation allows to extract tract-specific masks. Preliminary data analysis of 140 infants imaged at 38–44 weeks post-menstrual age.
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