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Title: Quantification of regional cardiac function : clinically-motivated algorithm development and application to cardiac magnetic resonance and computed tomography
Author: Vigneault, Davis Marc
ISNI:       0000 0004 6501 1018
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2017
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Techniques described to date for the reproducible and noninvasive quantification of regional cardiac function have been largely relegated to research settings due to time-consuming and cumbersome image acquisition and analysis. In this thesis, feature tracking algorithms are developed for 2-D+Time cardiac magnetic resonance (CMR) and 3-D+Time cardiac computed tomography (CCT) image sequences that are easily acquired clinically, while emphasising reproducibility and automation in their design. First, a commercially-implemented CMR feature tracking algorithm for the analysis of steady state free precession (SSFP) cine series is evaluated in patients with hypertrophic cardiomyopathy (HCM) and arrhythmogenic right ventricular cardiomyopathy (ARVC), which primarily affect the left ventricle (LV) and right ventricle (RV), respectively, and functional impairment compared with control populations is found in both cases. The limitations of this implementation are then used to guide development of an automated algorithm for the same purpose, making use of fully convolutional neural networks (CNN) for segmentation and spline registration across all frames simultaneously for tracking. This study is performed in the subjects with HCM, and functional impairment is again identified in disease subjects. Finally, as myocardial contraction is inherently a 3-D phenomenon, a technique is developed for quantification of regional function from 3-D+Time functional CCT studies using simultaneous registration of automatically generated Loop subdivision surface models for tracking. This study is performed in canine mongrels, and compared with the current state of the art technique for CCT functional analysis. This work demonstrates the feasibility of automated, reproducible cardiac functional analysis from CMR and CCT image sequences. While work remains to be done in extending the principles demonstrated and modular components described to fully automated whole-heart analysis, it is hoped that this thesis will accelerate the clinical adoption of regional functional analysis.
Supervisor: Noble, J. Alison Sponsor: National Institutes of Health
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Medical Image Analysis ; Regional Cardiac Function ; Cardiac Magnetic Resonance ; Deep Learning ; Mesh Registration ; Cardiac Computed Tomography