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A new presentation and exploration of human cerebral vasculature correlated with surface and sectional neuroanatomy
Author(s) -
Nowinski Wieslaw L.,
Thirunavuukarasuu Arumugam,
Volkau Ihar,
Marchenko Yevgen,
Aminah Bivi,
Gelas Arnaud,
Huang Su,
Lee Looi Chow,
Liu Jimin,
Ng Ting Ting,
Nowinska Natalia G.,
Qian Guoyu Yu,
Puspitasari Fiftarina,
Runge Val M.
Publication year - 2009
Publication title -
anatomical sciences education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.126
H-Index - 51
eISSN - 1935-9780
pISSN - 1935-9772
DOI - 10.1002/ase.68
Subject(s) - neuroanatomy , computer science , visualization , compositing , brain atlas , presentation (obstetrics) , animation , artificial intelligence , human–computer interaction , computer vision , neuroscience , computer graphics (images) , medicine , radiology , biology , image (mathematics)
The increasing complexity of human body models enabled by advances in diagnostic imaging, computing, and growing knowledge calls for the development of a new generation of systems for intelligent exploration of these models. Here, we introduce a novel paradigm for the exploration of digital body models illustrating cerebral vasculature. It enables dynamic scene compositing, real‐time interaction combined with animation, correlation of 3D models with sectional images, quantification as well as 3D manipulation‐independent labeling and knowledge‐related meta labeling (with name, diameter, description, variants, and references). This novel exploration is incorporated into a 3D atlas of cerebral vasculature with arteries and veins along with the surrounding surface and sectional neuroanatomy derived from 3.0 Tesla scans. This exploration paradigm is useful in medical education, training, research, and clinical applications. It enables development of new generation systems for rapid and intelligent exploration of complicated digital body models in real time with dynamic scene compositing from highly parcellated 3D models, continuous navigation, and manipulation‐independent labeling with multiple features. Anat Sci Ed 2:24–33, 2009. © 2009 American Association of Anatomists.