HumanBrainAtlas: an in vivo MRI dataset for detailed segmentations

التفاصيل البيبلوغرافية
العنوان: HumanBrainAtlas: an in vivo MRI dataset for detailed segmentations
المؤلفون: Mark M. Schira, Zoey J Isherwood, Mustafa (Steve) Kassem, Markus Barth, Thomas B. Shaw, Michelle M Roberts, George Paxinos
بيانات النشر: Cold Spring Harbor Laboratory, 2022.
سنة النشر: 2022
الوصف: We introduce HumanBrainAtlas, an initiative to construct a highly detailed, open-access atlas of the living human brain that combines high-resolutionin vivoMR 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 mm3isotropic 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 ofin vivoMRI. For example, components of the thalamus, hypothalamus, and hippocampus - difficult or 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 existingin vivoNeuroimaging analysis tools. The dataset is suitable for teaching and is publicly available via our website (www.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.
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::0580bda26af519c30a066e8169ff3d85
https://doi.org/10.1101/2022.10.16.511844
رقم الأكسشن: edsair.doi...........0580bda26af519c30a066e8169ff3d85
قاعدة البيانات: OpenAIRE