Visualization of the Cardiac Excitation and PVC Arrhythmia on a 3D Heart Model

Visualization of the cardiac potential movement is important in understanding the physiology of the human heart. A 3D visualization tool will help the cardiology students and others interested in human physiology to understand the functioning of the heart. In this thesis, such a tool is proposed which helps in the visualization of the cardiac potential movement and Premature Ventricular Contraction (PVC) event on a 3D heart model. The cardiac excitation obtained from a limb lead and a precordial lead of a 12 lead electrocardiograph (ECG) is mapped on a 3D heart model with fixed conduction pathways. The 3D heart model is obtained by modifying an existing anatomically accurate heart model. Fixed conduction pathways are defined on this derived 3D heart model. Each component of the ECG corresponds to the potential movement along each segment of these conduction pathways. The timing information from the limb lead signal is used to map the position of the cardiac potential on these conduction pathways. Amplitude and the timing information obtained from the precordial lead is mapped on a vector which points towards the corresponding precordial electrode on a separate window. This helps in understanding the instantaneous position of the cardiac potential on the transverse plane. Mapping of the cardiac excitation on the conduction pathways will stop and the color map of the heart will change during the occurrence of a PVC event. MIT-BIH arrhythmia database signals with at least one PVC wave were considered as input signal. It is observed that the system was able to detect PVC approximately 95% of the time (for the selected sample signals) and was able to map each ECG component accurately on the conduction pathways with minimum mapping delay

Calculating 3D intramyocardial strain tensors in a single slice of myocardium using MRI

Includes bibliographical references (leaves 108-112).Strain is a measure of cardiac deformation and provides information on the mechanical and functional properties of the heart. As this deformation occurs in three dimensions (3D), a 3D measure of strain is appropriate, however, currently the procedures for measuring 3D intramyocardial strain fields are limited to a handful of techniques. The only widely accepted method being the use of tagging in orthogonal image planes that requires the imaging of the entire myocardial volume, followed by lengthy and time consuming post processing. A method to combine cine displacement encoding with stimulated echoes (cine-DENSE) and cine strain encoded MRI (cine-SENC) for the formulation of the complete 3D strain tensor field for a single slice of myocardium is proposed

Scientific visualization of stress tensor information with applications to stress determination by X-ray and neutron diffraction

Includes bibliographical references (leaves 232-249).The visual analysis of mechanical stress facilitates physical understanding of the tensor quantity which is concealed in scalar and vector methods. In this study, the principles and techniques of scientific visualization are used to develop a visual analysis of mechanical stresses. Scientific visualization is not only applied to the final tensorial quantity obtained from the diffraction measurements, but the visual methods are developed from, and integrated into current residual stress analysis practices by relating the newly developed visual techniques to the conventional techniques, highlighting its advantages. This study consists of the mathematical analysis of the tensor character of mechanical stresses, discussion of the principles and techniques of scientific visualization (visual data analysis) in physical research, and tensor determination, visual analysis and presentation of residual stresses obtained from diffraction measurements

Statistical analysis for longitudinal MR imaging of dementia

Serial Magnetic Resonance (MR) Imaging can reveal structural atrophy in the brains of subjects with neurodegenerative diseases such as Alzheimer’s Disease (AD). Methods of computational neuroanatomy allow the detection of statistically significant patterns of brain change over time and/or over multiple subjects. The focus of this thesis is the development and application of statistical and supporting methodology for the analysis of three-dimensional brain imaging data. There is a particular emphasis on longitudinal data, though much of the statistical methodology is more general. New methods of voxel-based morphometry (VBM) are developed for serial MR data, employing combinations of tissue segmentation and longitudinal non-rigid registration. The methods are evaluated using novel quantitative metrics based on simulated data. Contributions to general aspects of VBM are also made, and include a publication concerning guidelines for reporting VBM studies, and another examining an issue in the selection of which voxels to include in the statistical analysis mask for VBM of atrophic conditions. Research is carried out into the statistical theory of permutation testing for application to multivariate general linear models, and is then used to build software for the analysis of multivariate deformation- and tensor-based morphometry data, efficiently correcting for the multiple comparison problem inherent in voxel-wise analysis of images. Monte Carlo simulation studies extend results available in the literature regarding the different strategies available for permutation testing in the presence of confounds. Theoretical aspects of longitudinal deformation- and tensor-based morphometry are explored, such as the options for combining within- and between-subject deformation fields. Practical investigation of several different methods and variants is performed for a longitudinal AD study

AA. The Visualization of Myocardial Strain for the Improved Analysis of Cardiac Mechanics

Heart diseases cause considerable morbidity and the prognosis after heart failure is poor. An improved understanding of cardiac mechanics is necessary to advance the diagnosis and treatment of heart diseases. This paper presents techniques for visualizing and evaluating biomedical finite element models and demonstrates their application by using as an example models of a healthy and a diseased human left ventricle. The following contributions are made: we apply techniques traditionally used in solid mechanics and computational fluid dynamics to biomedical data and suggest some improvements and modifications. We introduce a novel algorithm for computing isosurfaces for scalar fields defined over curvilinear finite elements. We obtain new insight into the mechanics of the healthy and the diseased left ventricle and we facilitate the understanding of the complex deformation of the heart muscle by novel visualizations