ELAINE Lecture Series
" Image-based modeling and simulation - Algorithmic Generation of Physiologically Realistic Patterns of Fibrosis "
Authors: David Jakes, Kevin Burrage, Christopher Drovandi, Pamela Burrage, Alfonso Bueno-Orovio, Blanca Rodriguez, Brodie Lawson (a mix of QUT and Oxford)
Presenter: Prof. Dr. Kevin Burrage, University of Oxford and Queensland University, Australia
Abstract:
Fibrosis, the pathological excess of fibroblast activity, is a significant health issue that hinders the function of many organs in the body, in some cases fatally. However, the severity of fibrosis-derived conditions depends on both the positioning of fibrotic affliction, and the microscopic patterning of fibroblast-deposited matrix proteins within affected regions. Variability in an individual's manifestation of a type of fibrosis is an important factor in explaining differences in symptoms, optimum treatment and prognosis, but a need for ex vivo procedures and a lack of experimental control over conflating factors has meant this variability remains poorly understood.
In this work, we present a computational methodology, based on Perlin noise fields, Fast Fourier Transforms and SMC ABC parameter estimation, for the generation of patterns of fibrosis microstructure. We demonstrate the technique using histological images of four types of cardiac fibrosis. Our generator and automated tuning method prove flexible enough to capture each of these very distinct patterns, allowing for rapid generation of new realisations for high-throughput computational studies. We also demonstrate via simulation, using the generated fibrotic patterns, the importance of micro-scale variability by showing significant differences in electrophysiological impact even within a single class of fibrosis, hence quantifying arrhythmic risk.
The key novel impact of our methodology is, through data enhancement and image based simulation, to remove limitations posed by the availability of ex-vivo data whilst being sophisticated enough to produce physiologically realistic patterns that match the data available and then to use image-based simulation to quantify arrhythmic risk.