Numerical simulation of Blood flow in the system of human coronary arteries with stenosis

This paper aims to present three-dimensional simulation of blood behavior in the system of human coronary arteries. The mathematical model is a set of partial differential equations including continuity equation and Navier-Stokes equations. The pulsatile conditions due to the heart pump during a cardiac cycle is imposed on the boundaries. Computational domain consists of the base of aorta, the left and the right coronary arteries. Finite element method is applied for the solution of the mathematical model Blood flow and temperature distribution in coronary system with normal arteries and stenosed arteries are computed. The results show that the appearance of stenosis reduces blood flow rate in the stenosed artery

Numerical Simulation of Blood Flow Through the System of Coronary Arteries with Diseased Left Anterior Descending

This paper aims to study the arterial stenosis effect on blood flow problem in the system of coronary arteries. Blood is assumed to be non-Newtonian incompressible fluid. The system of coronary arteries with diseased Left Anterior Descending (LAD) is considered. Governing equations are the Navier-Stokes equations and continuity equation subjected to the time-dependent pulsatile boundary conditions. Based on finite element method, the solution of the governing equations is solved numerically. Disturbances of blood flow through the diseased LAD for the restrictions of 25%, 50% and 75% are investigated. Flow characteristics, wall pressure and wall shear ratehave been studied in detail. Numerical studies show that blood flow with high speed and pressure rapidly drops in the area supplied by the stenosed artery. As the degree of coronary-artery stenosis increases, the maximal coronary flow decreases

Abdominal Aortic Aneurysm Segmentation Using Convolutional Neural Networks Trained with Images Generated with a Synthetic Shape Model

An abdominal aortic aneurysm (AAA) is a ballooning of the abdominal aorta, that if not treated tends to grow and rupture. Computed Tomography Angiography (CTA) is the main imaging modality for the management of AAAs, and segmenting them is essential for AAA rupture risk and disease progression assessment. Previous works have shown that Convolutional Neural Networks (CNNs) can accurately segment AAAs, but have the limitation of requiring large amounts of annotated data to train the networks. Thus, in this work we propose a methodology to train a CNN only with images generated with a synthetic shape model, and test its generalization and ability to segment AAAs from new original CTA scans. The synthetic images are created from realistic deformations generated by applying principal component analysis to the deformation fields obtained from the registration of few datasets. The results show that the performance of a CNN trained with synthetic data to segment AAAs from new scans is comparable to the one of a network trained with real images. This suggests that the proposed methodology may be applied to generate images and train a CNN to segment other types of aneurysms, reducing the burden of obtaining large annotated image databases