Computational evaluation of smoothed particle hydrodynamics for implementing blood flow modelling through CT reconstructed arteries

Investigation on aortic hemodynamics based on physics-informed neural network

Pressure in arteries is difficult to measure non-invasively. Although computational fluid dynamics (CFD) provides high-precision numerical solutions according to the basic physical equations of fluid mechanics, it relies on precise boundary conditions and complex preprocessing, which limits its real-time application. Machine learning algorithms have wide applications in hemodynamic research due to their powerful learning ability and fast calculation speed. Therefore, we proposed a novel method for pressure estimation based on physics-informed neural network (PINN). An ideal aortic arch model was established according to the geometric parameters from human aorta, and we performed CFD simulation with two-way fluid-solid coupling. The simulation results, including the space-time coordinates, the velocity and pressure field, were obtained as the dataset for the training and validation of PINN. Nondimensional Navier-Stokes equations and continuity equation were employed for the loss function of PINN, to calculate the velocity and relative pressure field. Post-processing was proposed to fit the absolute pressure of the aorta according to the linear relationship between relative pressure, elastic modulus and displacement of the vessel wall. Additionally, we explored the sensitivity of the PINN to the vascular elasticity, blood viscosity and blood velocity. The velocity and pressure field predicted by PINN yielded good consistency with the simulated values. In the interested region of the aorta, the relative errors of maximum and average absolute pressure were 7.33% and 5.71%, respectively. The relative pressure field was found most sensitive to blood velocity, followed by blood viscosity and vascular elasticity. This study has proposed a method for intra-vascular pressure estimation, which has potential significance in the diagnosis of cardiovascular diseases.

Classification of lungs infected COVID-19 images based on inception-ResNet

OBJECTIVE

Nowadays, COVID-19 is spreading rapidly worldwide, and seriously threatening lives . From the perspective of security and economy, the effective control of COVID-19 has a profound impact on the entire society. An effective strategy is to diagnose earlier to prevent the spread of the disease and prompt treatment of severe cases to improve the chance of survival.

METHODS

The method of this paper is as follows: Firstly, the collected data set is processed by chest film image processing, and the bone removal process is carried out in the rib subtraction module. Then, the set preprocessing method performed histogram equalization, sharpening, and other preprocessing operations on the chest film. Finally, shallow and high-level feature mapping through the backbone network extracts the processed chest radiographs. We implement the self-attention mechanism in Inception-Resnet, perform the standard classification, and identify chest radiograph diseases through the classifier to realize the auxiliary COVID-19 diagnosis process at the medical level, all in an effort to further enhance the classification performance of the convolutional neural network. Numerous computer simulations demonstrate that the Inception-Resnet convolutional neural network performs CT image categorization and enhancement with greater efficiency and flexibility than conventional segmentation techniques.

RESULTS

The experimental COVID-19 CT dataset obtained in this paper is the new data for CT scans and medical imaging of normal, early COVID-19 patients and severe COVID-19 patients from Jinyintan hospital. The experiment plots the relationship between model accuracy, model loss and epoch, using ACC, TPR, SPE, F1 score and G-mean to measure the image maps of patients with and without the disease. Statistical measurement values are obtained by Inception-Resnet are 88.23%, 83.45%, 89.72%, 95.53% and 88.74%. The experimental results show that Inception-Resnet plays a more effective role than other image classification methods in evaluation indicators, and the method has higher robustness, accuracy and intuitiveness.

CONCLUSION

With CT images in the clinical diagnosis of COVID-19 images being widely used and the number of applied samples continuously increasing, the method in this paper is expected to become an additional diagnostic tool that can effectively improve the diagnostic accuracy of clinical COVID-19 images.

Clinical Impact of Computational Heart Valve Models

This paper provides a review of engineering applications and computational methods used to analyze the dynamics of heart valve closures in healthy and diseased states. Computational methods are a cost-effective tool that can be used to evaluate the flow parameters of heart valves. Valve repair and replacement have long-term stability and biocompatibility issues, highlighting the need for a more robust method for resolving valvular disease. For example, while fluid-structure interaction analyses are still scarcely utilized to study aortic valves, computational fluid dynamics is used to assess the effect of different aortic valve morphologies on velocity profiles, flow patterns, helicity, wall shear stress, and oscillatory shear index in the thoracic aorta. It has been analyzed that computational flow dynamic analyses can be integrated with other methods to create a superior, more compatible method of understanding risk and compatibility.

Fluid-Structure Interaction Analyses of Biological Systems Using Smoothed-Particle Hydrodynamics

Due to the inherent complexity of biological applications that more often than not include fluids and structures interacting together, the development of computational fluid-structure interaction models is necessary to achieve a quantitative understanding of their structure and function in both health and disease. The functions of biological structures usually include their interactions with the surrounding fluids. Hence, we contend that the use of fluid-structure interaction models in computational studies of biological systems is practical, if not necessary. The ultimate goal is to develop computational models to predict human biological processes. These models are meant to guide us through the multitude of possible diseases affecting our organs and lead to more effective methods for disease diagnosis, risk stratification, and therapy. This review paper summarizes computational models that use smoothed-particle hydrodynamics to simulate the fluid-structure interactions in complex biological systems.

Hemodynamic Changes in the Carotid Artery after Infusion of Normal Saline Using Computational Fluid Dynamics

Purpose: To study the effect of the infusion of normal saline on hemodynamic changes in healthy volunteers using computational fluid dynamics (CFD) simulation. Methods: Eight healthy subjects participated and 16 carotid arteries were used for the CFD analysis. A one-liter intravenous infusion of normal saline was applied to the participants to observe the hemodynamic variations. Blood viscosity was measured before and after the injection of normal saline to apply the blood properties on the CFD modeling. Blood viscosity, shear rate, and wall shear stress were visually and quantitatively shown for the comparison between before and after the infusion of normal saline. Statistical analyses were performed to confirm the difference between the before and after groups. Results: After the infusion of normal saline, decreased blood viscosity was observed in the whole carotid artery. At the internal carotid artery, the recirculation zone with low intensity was found after the injection of normal saline. Increased shear rate and reduced wall shear stress was observed at the carotid bifurcation and internal carotid artery. The hemodynamic differences between before and after groups were statistically significant. Conclusions: The infusion of normal saline affected not only the overall changes of blood flow in the carotid artery but also the decrease of blood viscosity.

PySpark-Based Optimization of Microwave Image Reconstruction Algorithm for Head Imaging Big Data on High-Performance Computing and Google Cloud Platform

For processing large-scale medical imaging data, adopting high-performance computing and cloud-based resources are getting attention rapidly. Due to its low–cost and non-invasive nature, microwave technology is being investigated for breast and brain imaging. Microwave imaging via space-time algorithm and its extended versions are commonly used, as it provides high-quality images. However, due to intensive computation and sequential execution, these algorithms are not capable of producing images in an acceptable time. In this paper, a parallel microwave image reconstruction algorithm based on Apache Spark on high-performance computing and Google Cloud Platform is proposed. The input data is first converted to a resilient distributed data set and then distributed to multiple nodes on a cluster. The subset of pixel data is calculated in parallel on these nodes, and the results are retrieved to a master node for image reconstruction. Using Apache Spark, the performance of the parallel microwave image reconstruction algorithm is evaluated on high-performance computing and Google Cloud Platform, which shows an average speed increase of 28.56 times on four homogeneous computing nodes. Experimental results revealed that the proposed parallel microwave image reconstruction algorithm fully inherits the parallelism, resulting in fast reconstruction of images from radio frequency sensor’s data. This paper also illustrates that the proposed algorithm is generalized and can be deployed on any master-slave architecture.

Health risk stratification based on computed tomography pulmonary artery obstruction index for acute pulmonary embolism

Early effective identification of high-risk patients for acute pulmonary embolism (APE) contributes to timely treatment. The pulmonary artery obstruction index (PAOI) in computed tomography angiography (CTA) is a semi-quantitative observation index, commonly used to evaluate the severity of a patient's condition. This study explores the ability of PAOI to assess the risk stratification of APE. Thirty patients with APE were analysed. They were classified according to the guidelines, and the PAOI and cardiovascular parameters were measured in CTA. The difference of PAOI between different risk stratification patients was compared, and the predictive value of the PAOI for high-risk stratification was evaluated by the receiver operating characteristic curve. The correlation between PAOI and cardiovascular parameters was also analysed by Spearman correlation analysis. The PAOI in low- and high-risk patients was (33.2 ± 18.6)% and (68.1 ± 11.8)% respectively, and the difference was statistically significant. The PAOI was strongly predictive for high-risk patients. The cut-off value was 52.5%, with a sensitivity of 100% and specificity of 81.0%. The PAOI was correlated with the main cardiovascular parameters. We conclude that the PAOI in CTA is helpful for assessing risk stratification in patients with APE, which contributes to the selection of both the treatment plan and prognostic evaluation.

Grid Generation for Rendering Realistic X-ray Images of Narrow Blood Vessels in Real-time Angiography Simulation

This paper proposes a method to generate grids to render realistic X-ray images of the narrow blood vessels in real-time angiography simulation. The vertexes of the narrow blood vessels are projected onto the image-rendering plane. The grids aligned in the vessel direction are generated using the projected boundary vertexes on the image-rendering plane. The average computation time of the entire simulation is reduced by 80.17% compared to the simulation using the uniform grids. The results of the questionnaire survey show that the rendered X-ray images are realistic and useful to be applied to the angiography simulation.

The effect of myocardial action potential duration on cardiac pumping efficacy: a computational study

BACKGROUND AND AIMS

Although studies on the relation between arrhythmias and the action potential duration (APD) have been carried out, most of them are based only on electrophysiological factors of the heart and lack experiments that consider cardiac mechanical and electromechanical characteristics. Therefore, we conducted this study to clarify the relevance of the shortening of APD of a cell in relation to the mechanical contraction activity of the heart and the associated risk of arrhythmia.

METHODS

The human ventricular model used in this study has two dynamic characteristics: electrophysiological conduction and mechanical contraction. The model simulating electrophysiological characteristics was consisted of lumped parameter circuit that can mimic the phenomenon of ion exchange through the cell membrane of myocyte and consisted of 214,319 tetrahedral finite elements. In contrast, the model simulating mechanical contraction characteristics was constructed to mimic cardiac contraction by means of the crossbridge of a myofilament and consisted of 14,720 hermite-based finite elements to represent a natural 3D curve of the cardiac surface. First, we performed a single cell simulation and the electrophysiological simulation according to the change of the APD by changing the electrical conductivity of the I Ks channel. Thus, we confirmed the correlation between APD and intracellular Ca2+ concentration. Then, we compared mechanical response through mechanical simulation using Ca2+ data from electrical simulation.

RESULTS

The APD and the sum of the intracellular Ca2+ concentrations showed a positive correlation. The shortened APD reduced the conduction wavelength of ventricular cells by shortening the plateau and early repolarization in myocardial cells. The decrease in APD reduced ventricular pumping efficiency by more than 60% as compared with the normal group (normal conditions). This change is caused by the decline of ventricular output owing to reduced ATP consumption during the crossbridge of myofilaments and decreased tension.

CONCLUSION

The shortening of APD owing to increased electrical conductivity of a protein channel on myocardial cells likely decreases the wavelength and the pumping efficiency of the ventricles. Additionally, it may increase tissue sensitivity to ventricular fibrillation, including reentry, and cause symptoms such as dyspnea and dizziness.

The effect of heart failure and left ventricular assist device treatment on right ventricular mechanics: a computational study

Background and aims

Although it is important to analyze the hemodynamic factors related to the right ventricle (RV) after left ventricular assist device (LVAD) implantation, previous studies have focused only on the alteration of the ventricular shape and lack quantitative analysis of the various hemodynamic parameters. Therefore, we quantitatively analyzed various hemodynamic parameters related to the RV under normal, heart failure (HF), and HF incorporated with continuous flow LVAD therapy by using a computational model.

Methods

In this study, we combined a three-dimensional finite element electromechanical model of ventricles, which is based on human ventricular morphology captured by magnetic resonance imaging (MRI) with a lumped model of the circulatory system and continuous flow LVAD function in order to construct an integrated model of an LVAD implanted-cardiovascular system. To induce systolic dysfunction, the magnitude of the calcium transient function under HF condition was reduced to 70% of the normal value, and the time constant was reduced by 30% of the normal value.

Results

Under the HF condition, the left ventricular end systolic pressure decreased, the left ventricular end diastolic pressure increased, and the pressure in the right atrium (RA), RV, and pulmonary artery (PA) increased compared with the normal condition. The LVAD therapy decreased the end-systolic pressure of the LV by 41%, RA by 29%, RV by 53%, and PA by 71%, but increased the right ventricular ejection fraction by 52% and cardiac output by 40%, while the stroke work was reduced by 67% compared with the HF condition without LVAD. The end-systolic ventricular tension and strain decreased with the LVAD treatment.

Conclusion

LVAD enhances CO and mechanical unloading of the LV as well as those of the RV and prevents pulmonary hypertension which can be induced by HF.

Peripheral vascular disease assessment in the lower limb: a review of current and emerging non-invasive diagnostic methods

BACKGROUND

Worldwide, at least 200 million people are affected by peripheral vascular diseases (PVDs), including peripheral arterial disease (PAD), chronic venous insufficiency (CVI) and deep vein thrombosis (DVT). The high prevalence and serious consequences of PVDs have led to the development of several diagnostic tools and clinical guidelines to assist timely diagnosis and patient management. Given the increasing number of diagnostic methods available, a comprehensive review of available technologies is timely in order to understand their limitations and direct future development effort.

MAIN BODY

This paper reviews the available diagnostic methods for PAD, CVI, and DVT with a focus on non-invasive modalities. Each method is critically evaluated in terms of sensitivity, specificity, accuracy, ease of use, procedure time duration, and training requirements where applicable.

CONCLUSION

This review emphasizes the limitations of existing methods, highlighting a latent need for the development of new non-invasive, efficient diagnostic methods. Some newly emerging technologies are identified, in particular wearable sensors, which demonstrate considerable potential to address the need for simple, cost-effective, accurate and timely diagnosis of PVDs.

Clinical validation and assessment of aortic hemodynamics using computational fluid dynamics simulations from computed tomography angiography

Background

Hemodynamic information including peak systolic pressure (PSP) and peak systolic velocity (PSV) carry an important role in evaluation and diagnosis of congenital heart disease (CHD). Since MDCTA cannot evaluate hemodynamic information directly, the aim of this study is to provide a noninvasive method based on a computational fluid dynamics (CFD) model, derived from multi-detector computed tomography angiography (MDCTA) raw data, to analyze the aortic hemodynamics in infants with CHD, and validate these results against echocardiography and cardiac catheter measurements.

Methods

This study included 25 patients (17 males, and 8 females; a median age of 2 years, range: 4 months–4 years) with CHD. All patients underwent both transthoracic echocardiography (TTE) and MDCTA within 2 weeks prior to cardiac catheterization. CFD models were created from MDCTA raw data. Boundary conditions were confirmed by lumped parameter model and transthoracic echocardiography (TTE). Peak systolic velocity derived from CFD models (PSV_CFD) was compared to TTE measurements (PSV_TTE), while the peak systolic pressure derived from CFD (PSP_CFD) was compared to catheterization (PSP_CC). Regions with low and high peak systolic wall shear stress (PSWSS) were also evaluated.

Results

PSV_CFD and PSP_CFD showed good agreements between PSV_TTE (r = 0.968, p < 0.001; mean bias = − 7.68 cm/s) and PSP_CC (r = 0.918, p < 0.001; mean bias = 1.405 mmHg). Regions with low and high PSWSS) can also be visualized. Skewing of velocity or helical blood flow was also observed at aortic arch in patients.

Conclusions

Our result demonstrated that CFD scheme based on MDCTA raw data is an accurate and convenient method in obtaining the velocity and pressure from aorta and displaying the distribution of PSWSS and flow pattern of aorta. The preliminary results from our study demonstrate the capability in combining clinical imaging data and novel CFD tools in infants with CHD and provide a noninvasive approach for diagnose of CHD such as coarctation of aorta in future.