School of Psychology, University of Wollongong, Wollongong, NSW, 2522, Australia.
Neuroscience Research Australia, Randwick, NSW, 2031, Australia.
Brain Struct Funct. 2023 Nov;228(8):1849-1863. doi: 10.1007/s00429-023-02653-8. Epub 2023 Jun 5.
We introduce HumanBrainAtlas, an initiative to construct a highly detailed, open-access atlas of the living human brain that combines high-resolution in vivo MR imaging and detailed segmentations previously possible only in histological preparations. Here, we present and evaluate the first step of this initiative: a comprehensive dataset of two healthy male volunteers reconstructed to a 0.25 mm isotropic resolution for T1w, T2w, and DWI contrasts. Multiple high-resolution acquisitions were collected for each contrast and each participant, followed by averaging using symmetric group-wise normalisation (Advanced Normalisation Tools). The resulting image quality permits structural parcellations rivalling histology-based atlases, while maintaining the advantages of in vivo MRI. For example, components of the thalamus, hypothalamus, and hippocampus are often impossible to identify using standard MRI protocols-can be identified within the present data. Our data are virtually distortion free, fully 3D, and compatible with the existing in vivo Neuroimaging analysis tools. The dataset is suitable for teaching and is publicly available via our website (hba.neura.edu.au), which also provides data processing scripts. Instead of focusing on coordinates in an averaged brain space, our approach focuses on providing an example segmentation at great detail in the high-quality individual brain. This serves as an illustration on what features contrasts and relations can be used to interpret MRI datasets, in research, clinical, and education settings.
我们介绍 HumanBrainAtlas,这是一项构建高度详细的、可公开获取的活体人脑图谱的计划,它结合了高分辨率的活体磁共振成像和以前只有在组织学准备中才可能实现的详细分割。在这里,我们展示并评估了该计划的第一步:一个由两名健康男性志愿者组成的综合数据集,其分辨率为 0.25 毫米各向同性,用于 T1w、T2w 和 DWI 对比。每个志愿者都采集了多个高分辨率的对比图像,然后使用对称的群组归一化(Advanced Normalisation Tools)进行平均。最终的图像质量允许进行与基于组织学的图谱相媲美的结构分割,同时保持活体 MRI 的优势。例如,使用标准 MRI 方案通常无法识别丘脑、下丘脑和海马体的组成部分,但在本数据中可以识别。我们的数据几乎没有失真,完全是 3D 的,并且与现有的活体神经影像学分析工具兼容。该数据集适合教学,可通过我们的网站(hba.neura.edu.au)公开获取,该网站还提供数据处理脚本。我们的方法不是专注于平均大脑空间中的坐标,而是专注于在高质量的个体大脑中以非常详细的方式提供一个示例分割。这可以作为在研究、临床和教育环境中解释 MRI 数据集时可以使用的对比度和关系特征的说明。