4.6 Article

HumanBrainAtlas: an in vivo MRI dataset for detailed segmentations

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BRAIN STRUCTURE & FUNCTION
卷 228, 期 8, 页码 1849-1863

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SPRINGER HEIDELBERG
DOI: 10.1007/s00429-023-02653-8

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MRI; Human brain; 7T; 3T; T1w; T2w; DWI; Image acquisition

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We introduce the HumanBrainAtlas, which aims to create a highly detailed and open-access atlas of the living human brain by combining high-resolution in vivo MR imaging with detailed segmentations previously only available in histological preparations. We have presented and evaluated the first step of this initiative, which includes a comprehensive dataset of two healthy male volunteers reconstructed to a 0.25 mm isotropic resolution for T1w, T2w, and DWI contrasts. The dataset is virtually distortion-free, fully 3D, and compatible with existing in vivo Neuroimaging analysis tools.
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.

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