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Multidimensional transformation of clinical imaging data for virtual reality learning objects
Author(s) -
Trelease Robert Bernard,
Rosset Antoine
Publication year - 2007
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.21.5.a86-a
Subject(s) - computer science , virtual reality , visualization , metadata , transformation (genetics) , cadaveric spasm , artificial intelligence , computer vision , computer graphics (images) , human–computer interaction , anatomy , medicine , world wide web , biochemistry , chemistry , gene
Three‐dimensional (3D) modeling and virtual reality (VR) methods have made computer‐based structural visualization a practical tool. As exemplified by the Visible Human, Digital Anatomist, and other data sets, cadaveric 3D anatomy has been mobilized for scientific, educational, and illustrative purposes. With the continuing emphasis on using clinical imaging to teach “live” anatomy, recent advances in image processing and resolution support the transformation of large 3D computerized tomography (CT) and magnetic resonance (MR) datasets for VR models and simulations. Furthermore, additional dimensions of functional data and metadata can be integrated with the structural, in order to produce VR “learning objects”, standardized interactive 3D multimedia modules that can be integrated into a variety of applications and Web‐based educational resources. We describe the processes for creating such VR learning objects from rendered and “stripped” CT and MR data sets, with specific examples of surface‐ and volume‐rendered head and neck, thoracic, and arterial visualizations. In addition to providing access to a range of normal variation and pathological live anatomy, the use of clinical imaging data speeds the production of VR models, compared with reconstructions from segmented cadaveric sections.