Predictive Physiological Modeling of Percutaneous Coronary Intervention - Is Virtual Treatment Planning the Future?

Patient-specific 3D in vitro modeling and fluid dynamic analysis of primary pulmonary vein stenosis

Introduction

Primary pulmonary vein stenosis (PVS) is a rare congenital heart disease that proves to be a clinical challenge due to the rapidly progressive disease course and high rates of treatment complications. PVS intervention is frequently faced with in-stent restenosis and persistent disease progression despite initial venous recanalization with balloon angioplasty or stenting. Alterations in wall shear stress (WSS) have been previously associated with neointimal hyperplasia and venous stenosis underlying PVS progression. Thus, the development of patient-specific three-dimensional (3D) in vitro models is needed to further investigate the biomechanical outcomes of endovascular and surgical interventions.

Methods

In this study, deidentified computed tomography images from three patients were segmented to generate perfusable phantom models of pulmonary veins before and after catheterization. These 3D reconstructions were 3D printed using a clear resin ink and used in a benchtop experimental setup. Computational fluid dynamic (CFD) analysis was performed on models in silico utilizing Doppler echocardiography data to represent the in vivo flow conditions at the inlets. Particle image velocimetry was conducted using the benchtop perfusion setup to analyze WSS and velocity profiles and the results were compared with those predicted by the CFD model.

Results

Our findings indicated areas of undesirable alterations in WSS before and after catheterization, in comparison with the published baseline levels in the healthy in vivo tissues that may lead to regional disease progression.

Discussion

The established patient-specific 3D in vitro models and the developed in vitro-in silico platform demonstrate great promise to refine interventional approaches and mitigate complications in treating patients with primary PVS.

Artificial Intelligence, Computational Simulations, and Extended Reality in Cardiovascular Interventions

Artificial intelligence, computational simulations, and extended reality, among other 21st century computational technologies, are changing the health care system. To collectively highlight the most recent advances and benefits of artificial intelligence, computational simulations, and extended reality in cardiovascular therapies, we coined the abbreviation AISER. The review particularly focuses on the following applications of AISER: 1) preprocedural planning and clinical decision making; 2) virtual clinical trials, and cardiovascular device research, development, and regulatory approval; and 3) education and training of interventional health care professionals and medical technology innovators. We also discuss the obstacles and constraints associated with the application of AISER technologies, as well as the proposed solutions. Interventional health care professionals, computer scientists, biomedical engineers, experts in bioinformatics and visualization, the device industry, ethics committees, and regulatory agencies are expected to streamline the use of AISER technologies in cardiovascular interventions and medicine in general.

Ionized and non-ionized radiation effects on coronary stent implantation

The purpose of this study is to examine the clinical radiation effects on patients with various stents. Several previous researches focused on the mechanical and physical features of the stents, but there have been few studies that focus on the interaction with radiological and clinical radiation. For stent material analysis, the ANSYS package program was employed. These materials and models are often built in three dimensions for three different types of stents made of three various materials. Estimates of blood pressure and thermal radiation were explored, as were the effects of non-ionizing radiation. Furthermore, the radiation attenuation characteristics of stent samples are investigated. The mass attenuation coefficient values are computed using MATLAB code over a large energy range of 0.015-15 MeV, and the findings are validated using theoretical WinXCom results. To determine the gamma-ray attenuation performances of the studied stent samples, variables such as the mass attenuation coefficient (MAC), half-value layer (HVL), tenth-value layer (TVL), mean free path (MFP), effective atomic number (Z_eff), exposure build-up factor (EBF), and energy absorption build-up factor (EABF) are computed. Effective removal cross-sections (Σ_R) of stent samples were acquired to determine the capacity of stent samples to stop fast neutrons. Finally, the ability of stent samples to stop charged alpha and proton particles was evaluated utilizing mass stopping power and projected range parameters. The discovery demonstrates that S3 has the best attenuation as well as the best proton, alpha, and gamma radiation attenuation capability.