Search Results (10)

Cardiovascular diseases (CVDs) are the most common health threats worldwide. 2D X-ray invasive coronary angiography (ICA) remains the most widely adopted imaging modality for CVD assessment during real-time cardiac interventions. However, it is often difficult for the cardiologists to interpret the 3D geometry of coronary vessels based on 2D planes. Moreover, due to the radiation limit, often only two angiographic projections are acquired, providing limited information of the vessel geometry and necessitating 3D coronary tree reconstruction based only on two ICA projections. In this paper, we propose a self-supervised deep learning method called NeCA, which is based on neural implicit representation using the multiresolution hash encoder and differentiable cone-beam forward projector layer, in order to achieve 3D coronary artery tree reconstruction from two 2D projections. We validate our method using six different metrics on a dataset generated from coronary computed tomography angiography of right coronary artery and left anterior descending artery. The evaluation results demonstrate that our NeCA method, without requiring 3D ground truth for supervision or large datasets for training, achieves promising performance in both vessel topology and branch-connectivity preservation compared to the supervised deep learning model. Full article

The estimation of vessels’ centerlines is a critical step in assessing the geometry of the vessel, the topological representation of the vessel tree, and vascular network visualization. In this research, we present a novel method for obtaining geometric parameters from peripheral arteries in 3D medical binary volumes. Our approach focuses on centerline extraction, which yields smooth and robust results. The procedure starts with a segmented 3D binary volume, from which a distance map is generated using the Euclidean distance transform. Subsequently, a skeleton is extracted, and seed points and endpoints are identified. A search methodology is used to derive the best path on the skeletonized 3D binary array while tracking from the goal points to the seed point. We use the distance transform to calculate the distance between voxels and the nearest vessel surface, while also addressing bifurcations when vessels divide into multiple branches. The proposed method was evaluated on 22 real cases and 10 synthetically generated vessels. We compared our method to different state-of-the-art approaches and demonstrated its better performance. The proposed method achieved an average error of 1.382 mm with real patient data and 0.571 mm with synthetic data, both of which are lower than the errors obtained by other state-of-the-art methodologies. This extraction of the centerline facilitates the estimation of multiple geometric parameters of vessels, including radius, curvature, and length. Full article

The evolution of 3D printing has ushered in accessibility and cost-effectiveness, spanning various industries including biomedical engineering, education, and microfluidics. In biomedical engineering, it encompasses bioprinting tissues, producing prosthetics, porous metal orthopedic implants, and facilitating educational models. Hybrid Additive Manufacturing approaches and, more specifically, the integration of Fused Deposition Modeling (FDM) with bio-inkjet printing offers the advantages of improved accuracy, structural support, and controlled geometry, yet challenges persist in cell survival, interaction, and nutrient delivery within printed structures. The goal of this study was to develop and present a low-cost way to produce physical phantoms of human organs that could be used for research and training, bridging the gap between the use of highly detailed computational phantoms and real-life clinical applications. To this purpose, this study utilized anonymized clinical Computed Tomography (CT) data to create a liver physical model using the Creality Ender-3 printer. Polylactic Acid (PLA), Polyvinyl Alcohol (PVA), and light-bodied silicone (Polysiloxane) materials were employed for printing the liver including its veins and arteries. In brief, PLA was used to create a mold of a liver to be filled with biocompatible light-bodied silicone. Molds of the veins and arteries were printed using PVA and then inserted in the liver model to create empty channel. In addition, the PVA was then washed out by the final product using warm water. Despite minor imperfections due to the printer’s limitations, the final product imitates the computational model accurately enough. Precision adjustments in the design phase compensated for this variation. The proposed novel low-cost 3D printing methodology successfully produced an anatomically accurate liver physical model, presenting promising applications in medical education, research, and surgical planning. Notably, its implications extend to medical training, personalized medicine, and organ transplantation. The technology’s potential includes injection training for medical professionals, personalized anthropomorphic phantoms for radiation therapy, and the future prospect of creating functional living organs for organ transplantation, albeit requiring significant interdisciplinary collaboration and financial investment. This technique, while showcasing immense potential in biomedical applications, requires further advancements and interdisciplinary cooperation for its optimal utilization in revolutionizing medical science and benefiting patient healthcare. Full article

Thanks to recent technological and IT advances, there have been rapid developments in biomedical and health research applications of computational fluid dynamics. This is a methodology of computer-based simulation that uses numerical solutions of the governing equations to simulate real fluid flows. The aim of this study is to investigate, using a patient-specific computational fluid dynamics analysis, the hemodynamic behavior of two arterial cannulae, with two different geometries, used in clinical practice during cardiopulmonary bypass. A realistic 3D model of the aorta is extracted from a subject’s CT images using segmentation and reverse engineering techniques. The two cannulae, with similar geometry except for the distal end (straight or curved tip), are modeled and inserted at the specific position in the ascending aorta. The assumption of equal boundary conditions is adopted for the two simulations in order to analyze only the effects of a cannula’s geometry on hemodynamic behavior. Simulation results showed a greater percentage of the total output directed towards the supra-aortic vessels with the curved tip cannula (66% vs. 54%), demonstrating that the different cannula tips geometry produces specific advantages during cardiopulmonary bypass. Indeed, the straight one seems to generate a steadier flow pattern with good recirculation in the ascending aorta. Full article

Cardiovascular diseases still represent one of the most deadly pathologies worldwide. Knowledge of the blood flow dynamics within the cardio-vascular system is crucial in preventing these diseases and analysing their physiology and physio-pathology. CFD simulations are highly effective in guiding clinical predictions and, more importantly, allow the evaluation of physical and clinical parameters that are difficult to measure with common diagnostic techniques. Therefore, in particular, this study is focused on investigating the hemodynamics of the thoracic aorta. Real aortic geometries regarding a sane and diseased patient presenting an aneurysm were considered. CFD simulations were performed with the OpenFOAM C++ library using patient-specific pulsatile blood flow waveforms and implementing the Windkessel pressure boundary condition for the artery outflow. The adopted methodology was preliminarily verified for assessing the numerical uncertainty and convergence. Then, the CFD results were evaluated against experimental data concerning pressure and velocity of the thoracic aorta measured with standard diagnostic techniques. The normal aorta’s blood flow was also compared against the pattern regarding the patient-specific aortic aneurysm. Parameters such as wall pressure, wall shear stress (WSS) and velocity distribution were investigated and discussed. The research highlighted that the blood flow in the aorta is strongly affected by the aneurysm onset, with the growth of recirculation zones being potentially hazardous. The outcomes of the investigation finally demonstrate how CFD simulation tools, capturing the detailed physics of the aortic flow, are powerful tools for supporting clinical activities of the cardio-vascular system. Full article

This study develops a data-driven reduced-order model based on a deep convolutional neural network (CNN) for real-time and accurate prediction of the drug trajectory and concentration field in transarterial chemoembolization therapy to assist in directing the drug to the tumor site. The convolutional and deconvoluational layers are used as the encoder and the decoder, respectively. The input of the network model is designed to contain the information of drug injection location and the blood vessel geometry and the output consists of the drug trajectory and the concentration field. We studied drug delivery in two-dimensional straight, bifurcated blood vessels and the human hepatic artery system and showed that the proposed model can quickly and accurately predict the spatial–temporal drug concentration field. For the human hepatic artery system, the most complex case, the average prediction accuracy was 99.9% compared with the CFD prediction. Further, the prediction time for each concentration field was less than 0.07 s, which is four orders faster than the corresponding CFD simulation. The high performance, accuracy and speed of the CNN model shows the potential for effectively assisting physicians in directing chemoembolization drugs to tumor-bearing segments, thus improving its efficacy in real-time. Full article

Arterial stenosis is a problem of immediate significance, as cardiovascular disease is the number one leading cause of death worldwide. Generally, the study of stenotic flow assumes a smooth, curved stenosis and artery. However, the real situation is unlikely to present an infinitely smooth-surfaced arterial stenosis. Here, the impact of surface roughness on the flow in an arterial stenosis was studied via a computational fluid dynamics analysis. A patient-specific geometry with a smooth surface was reconstructed, and a partially rough model was built by artificially adding random roughness only on the stenotic region of the smooth model. It was found that the flow was oscillatory downstream of the stenosis in the models. A slightly lower velocity near the wall and more oscillatory flows were observed due to the presence of the roughness in the stenotic region. However, the pressure distributions did not vary significantly between the smooth and rough models. The differences in the wall shear metrics were slight in the stenotic region and became larger in the downstream region of the models. Full article

The present work aims at developing a numerical study on the drug diffusion in the bloodstream in a coronary artery with drug-eluting stent implanted. The blood was modeled as a single-phase, incompressible and Newtonian fluid and the Navier–Stokes equation was approximated according to the Finite Element Method (FEM). The dynamics of drug-eluting concentration in bloodstream was investigated using four drug-eluting stents with different mass diffusivities in microchannels with variable cross sections, including a real coronary artery geometry with atherosclerosis. The results reveal complex drug concentration patterns and accumulation in the vicinity of the fat buildup. Full article

In arterial thrombosis, von Willebrand factor (VWF) bridges platelets to sites of vascular injury. The adhesive properties of VWF are controlled by its different domains, which may be engineered into ligands for targeting nanoparticles to vascular injuries. Here, we functionalized 200 nm polystyrene nanoparticles with the VWF-A1 domain and studied their spatial adhesion to collagen or collagen-VWF coated, real-sized coronary stenosis models under physiological flow. When VWF-A1 nano-particles (A1-NPs) were perfused through a 75% stenosis model coated with collagen-VWF, the particles preferentially adhered at the post stenotic region relative to the pre-stenosis region while much less adhesion was detected at the stenosis neck (~ 65-fold less). When infused through collagen-coated models or when the A1 coating density of nanoparticles was reduced by 100-fold, the enhanced adhesion at the post-stenotic site was abolished. In a 60% stenosis model, the adhesion of A1-NPs to collagen-VWF-coated models depended on the location examined within the stenosis. Altogether, our results indicate that VWF-A1 NPs exhibit a flow-structure dependent adhesion to VWF and illustrate the important role of studying cardiovascular nano-medicines in settings that closely model the size, geometry, and hemodynamics of pathological environments. Full article

The inflammation and lipid accumulation in the arterial wall represents a progressive disease known as atherosclerosis. In this study, a numerical model of atherosclerosis progression was developed. The wall shear stress (WSS) and blood analysis data have a big influence on the development of this disease. The real geometry of patients, and the blood analysis data (cholesterol, HDL, LDL, and triglycerides), used in this paper, was obtained within the H2020 SMARTool project. Fluid domain (blood) was modeled using Navier-Stokes equations in conjunction with continuity equation, while the solid domain (arterial wall) was modeled using Darcy’s law. For the purpose of modeling low-density lipoprotein (LDL) and oxygen transport, convection-diffusion equations were used. Kedem-Katchalsky equations were used for coupling fluid and solid dynamics. Full article