Real-time Medical Visualization of Human Head and Neck Anatomy and its Applications for Dental Training and Simulation

The Digital Design Studio and NHS Education Scotland have developed ultra-high definition real-time interactive 3D anatomy of the head and neck for dental teaching, training and simulation purposes. In this paper we present an established workflow using state-of-the-art 3D laser scanning technology and software for design and construction of medical data and describe the workflow practices and protocols in the head and neck anatomy project. Anatomical data was acquired through topographical laser scanning of a destructively dissected cadaver. Each stage of model development was clinically validated to produce a normalised human dataset which was transformed into a real-time environment capable of large-scale 3D stereoscopic display in medical teaching labs across Scotland, whilst also supporting single users with laptops and PC. Specific functionality supported within the 3D Head and Neck viewer includes anatomical labelling, guillotine tools and selection tools to expand specific local regions of anatomy. The software environment allows thorough and meaningful investigation to take place of all major and minor anatomical structures and systems whilst providing the user with the means to record sessions and individual scenes for learning and training purposes. The model and software have also been adapted to permit interactive haptic simulation of the injection of a local anesthetic

Real-time Medical Visualization of Human Head and Neck Anatomy and its Applications for Dental Training and Simulation

The Digital Design Studio and NHS Education Scotland have developed ultra-high definition real-time interactive 3D anatomy of the head and neck for dental teaching, training and simulation purposes. In this paper we present an established workflow using state-of-the-art 3D laser scanning technology and software for design and construction of medical data and describe the workflow practices and protocols in the head and neck anatomy project. Anatomical data was acquired through topographical laser scanning of a destructively dissected cadaver. Each stage of model development was clinically validated to produce a normalised human dataset which was transformed into a real-time environment capable of large-scale 3D stereoscopic display in medical teaching labs across Scotland, whilst also supporting single users with laptops and PC. Specific functionality supported within the 3D Head and Neck viewer includes anatomical labelling, guillotine tools and selection tools to expand specific local regions of anatomy. The software environment allows thorough and meaningful investigation to take place of all major and minor anatomical structures and systems whilst providing the user with the means to record sessions and individual scenes for learning and training purposes. The model and software have also been adapted to permit interactive haptic simulation of the injection of a local anaesthetic

Development and characterization of methodology and technology for the alignment of fMRI time series

This dissertation has developed, implemented and tested a novel computer based system (AUTOALIGN) that incorporates an algorithm for the alignment of functional Magnetic Resonance Image (fMRI) time series. The algorithm assumes the human brain to be a rigid body and computes a head coordinate system on the basis of three reference points that lie on the directions correspondent to two of the eigenvectors of inertia of the volume, at the intersections with the head boundary. The eigenvectors are found weighting the inertia components with the voxel\u27s intensity values assumed as mass. The three reference points are found in the same position, relative to the origin of the head coordinate system, in both test and reference brain images. Intensity correction is performed at sub-voxel accuracy by tri-linear interpolation. A test fMR brain volume in which controlled simulations of rigid-body transformations have been introduced has preliminarily assessed system performance. Further experimentation has been conducted with real fMRI time series. Rigid-body transformations have been retrieved automatically and the values of the motion parameters compared to those obtained by the Statistical Parametric Mapping (SPM99), and the Automatic Image Registration (AIR 3.08). Results indicated that AUTOALIGN offers subvoxel accuracy in correcting both misalignment and intensity among time points in fMR images time series, and also that its performance is comparable to that of SPM99 and AIR3.08