Perlin Noise Generation of Physiologically Realistic Patterns of Fibrosis

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 afflicted 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 for the generation of patterns of fibrosis microstructure, demonstrating 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 microscale variability by showing significant differences in electrophysiological impact even within a single class of fibrosis.

Fibrotic Remodeling during Persistent Atrial Fibrillation: In Silico Investigation of the Role of Calcium for Human Atrial Myofibroblast Electrophysiology

“…Tissue simulations showed that not only could the myofibroblast infiltration affect the arrhythmia vulnerability [ 73 , 74 ], but also the fibrosis pattern can play a role in the initiation and maintenance of reentrant activity [ 46 , 47 , 54 , 75 ]. Sridhar et al [ 76 ] have shown the role that fibroblast infiltration plays regarding the onset of arrhythmia due to the increase of anisotropy in the tissue.…”

“…Tissue simulations were carried in a patch of 50 mm × 50 mm × 3 mm with a central fibrotic area (radius 10 mm) featuring 11 different fibrosis patterns. The patterns were generated as Perlin noise [ 54 ], which is used in computer-generated imagery to generate realistic textures [ 55 ]. These textures were used to distribute the myofibroblasts following the fibrotic patterns (Figure 5) observed in MRI and histological cuts from interstitial and patchy fibrotic tissue [ 56 , 57 ].…”

Using Machine Learning to Characterize Atrial Fibrotic Substrate From Intracardiac Signals With a Hybrid in silico and in vivo Dataset

“…Several studies have shown the importance of the texture of the fibrotic tissue for excitation propagation in the cardiac substrate (Jakes et al, 2019 ; Dokuchaev et al, 2020 ; Nezlobinsky et al, 2020 ). Our proposed model aims at reproducing the deposited collagen fibers observed in tissue samples with interstitial fibrosis (Hansen et al, 2017 ).…”

Using Machine Learning to Characterize Atrial Fibrotic Substrate from Intracardiac Signals using a Hybrid in silico and in vivo Dataset

“…Several studies have shown the importance of the texture of the fibrotic tissue for excitation propagation in the cardiac substrate [52][53][54] . Our proposed model aims at reproducing the deposited collagen fibers observed in tissue samples with interstitial fibrosis 55 .…”