Ricardo
(2022-06-01 00:45):
#paper https://doi.org/10.1016/j.neuroimage.2022.119097 A 4D infant brain volumetric atlas based on the UNC/UMN baby connectome project (BCP) cohort. 2022年发表于neuroimage。由于人类大脑在出生后的头两年处于快速发育的过程,随着年龄的增长,其MRI影像的图像appearance和contrast呈现动态的变化。因此,为婴幼儿早期发育研究构建高精度的时空脑图谱是一件非常重要的事情。这篇研究从240名26月龄以前的婴幼儿被试中采集了542例T1和T2的纵向影像数据用于图谱的构建。出乎我意料的是,他们没有采用他们实验室之前开发的一系列针对于婴幼儿脑影像数据特点的配准技术,而是通过结合强度图像和分割图像并利用基于成人大脑开发的配准算法构建的图谱。他们对0-24个月的婴幼儿分年龄段的构建了17个时间点的图谱,其中前12个月每一个月构建一个图谱,后12个月每3个月构建一个图谱。当然这篇文章存在一些技术问题,我的博士课题也正在考虑做相似的工作,可能会根据里面出现的问题做一些改进。
A 4D infant brain volumetric atlas based on the UNC/UMN baby connectome project (BCP) cohort
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Abstract:
Spatiotemporal (four-dimensional) infant-dedicated brain atlases are essential for neuroimaging analysis of early dynamic brain development. However, due to the substantial technical challenges in the acquisition and processing of infant brain MR images, 4D atlases densely covering the dynamic brain development during infancy are still scarce. Few existing ones generally have fuzzy tissue contrast and low spatiotemporal resolution, leading to degraded accuracy of atlas-based normalization and subsequent analyses. To address this issue, in this paper, we construct a 4D structural MRI atlas for infant brains based on the UNC/UMN Baby Connectome Project (BCP) dataset, which features a high spatial resolution, extensive age-range coverage, and densely sampled time points. Specifically, 542 longitudinal T1w and T2w scans from 240 typically developing infants up to 26-month of age were utilized for our atlas construction. To improve the co-registration accuracy of the infant brain images, which typically exhibit dynamic appearance with low tissue contrast, we employed the state-of-the-art registration method and leveraged our generated reliable brain tissue probability maps in addition to the intensity images to improve the alignment of individual images. To achieve consistent region labeling on both infant and adult brain images for facilitating region-based analysis across ages, we mapped the widely used Desikan cortical parcellation onto our atlas by following an age-decreasing mapping manner. Meanwhile, the typical subcortical structures were manually delineated to facilitate the studies related to the subcortex. Compared with the existing infant brain atlases, our 4D atlas has much higher spatiotemporal resolution and preserves more structural details, and thus can boost accuracy in neurodevelopmental analysis during infancy.
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