响马读paper

一个要求成员每月至少读一篇文献并打卡的学术交流社群

2022, NeuroImage. DOI: 10.1016/j.neuroimage.2022.119097
A 4D infant brain volumetric atlas based on the UNC/UMN baby connectome project (BCP) cohort
Liangjun Chen , Zhengwang Wu , Dan Hu , Ya Wang , Fenqiang Zhao , Tao Zhong , Weili Lin , Li Wang , Gang Li
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.
2022-12-31 19:22:00
#paper A 4D infant brain volumetric atlas based on the UNC/UMN baby connectome project (BCP) cohort,NeuroImage,2022,https://doi.org/10.1016/j.neuroimage.2022.119097 时空婴儿脑图谱对于分析早期动态大脑发育至关重要。 但由于婴儿大脑 MR 图像的收集和处理存在巨大挑战,紧密覆盖婴儿期大脑动态发育各个年龄段的4D 图谱仍然很少。 现有的图谱存在组织对比度和低时空分辨率的问题,使得后续分析的准确性下降。 为了解决这个问题,本文基于 UNC/UMN Baby Connectome Project (BCP) 数据集构建了婴儿大脑的 4D 结构 MRI 图谱,该数据集具有高空间分辨率、广泛的年龄范围和密集的采样时间 点。 为了提高联合配准的精确度,采用了最先进的配准方法,并利用脑组织概率图以及强度图像改善单个图像的对齐方式。 为了在婴儿和成人脑图像上实现一致的区域标记以促进跨年龄的基于区域的分析,通过遵循年龄递减的映射方式将广泛使用的 Desikan 皮层分割映射到我们的图谱上。 同时,人工勾画出了典型的皮层下结构,方便皮层下相关研究。 与现有的婴儿脑图谱相比,本文图谱具有更高的时空分辨率并保留了更多的结构细节,因此可以提高婴儿期神经发育分析的准确性。
TOP