Yogesh Karnam, Fernando Mut, Alexander K. Yu, Boyle Cheng, Sepideh Amin-Hanjani, Fady T. Charbel, Henry H. Woo, Mika Niemelä, Riikka Tulamo, Behnam Rezai Jahromi, Juhana Frösen, Yasutaka Tobe, Anne M. Robertson, Juan R. Cebral
Intracranial aneurysms (IAs) pose severe health risks influenced by hemodynamics. This study focuses on the intricate characterization of hemodynamic conditions within the IA walls and their influence on bleb development, aiming to enhance understanding of aneurysm stability and the risk of rupture. The methods emphasized utilizing a comprehensive dataset of 359 IAs and 213 IA blebs from 268 patients to reconstruct patient-specific vascular models, analyzing blood flow using finite element methods to solve the unsteady Navier–Stokes equations, the segmentation of aneurysm wall subregions and the hemodynamic metrics wall shear stress (WSS), its metrics, and the critical points in WSS fields were computed and analyzed across different aneurysm subregions defined by saccular, streamwise, and topographical divisions. The results revealed significant variations in these metrics, correlating distinct hemodynamic environments with wall features on the aneurysm walls, such as bleb formation. Critical findings indicated that regions with low WSS and high OSI, particularly in the body and central regions of aneurysms, are prone to conditions that promote bleb formation. Conversely, areas exposed to high WSS and positive divergence, like the aneurysm neck, inflow, and outflow regions, exhibited a different but substantial risk profile for bleb development, influenced by flow impingements and convergences. These insights highlight the complexity of aneurysm behavior, suggesting that both high and low-shear environments can contribute to aneurysm pathology through distinct mechanisms.
{"title":"Description of the local hemodynamic environment in intracranial aneurysm wall subdivisions","authors":"Yogesh Karnam, Fernando Mut, Alexander K. Yu, Boyle Cheng, Sepideh Amin-Hanjani, Fady T. Charbel, Henry H. Woo, Mika Niemelä, Riikka Tulamo, Behnam Rezai Jahromi, Juhana Frösen, Yasutaka Tobe, Anne M. Robertson, Juan R. Cebral","doi":"10.1002/cnm.3844","DOIUrl":"10.1002/cnm.3844","url":null,"abstract":"<p>Intracranial aneurysms (IAs) pose severe health risks influenced by hemodynamics. This study focuses on the intricate characterization of hemodynamic conditions within the IA walls and their influence on bleb development, aiming to enhance understanding of aneurysm stability and the risk of rupture. The methods emphasized utilizing a comprehensive dataset of 359 IAs and 213 IA blebs from 268 patients to reconstruct patient-specific vascular models, analyzing blood flow using finite element methods to solve the unsteady Navier–Stokes equations, the segmentation of aneurysm wall subregions and the hemodynamic metrics wall shear stress (WSS), its metrics, and the critical points in WSS fields were computed and analyzed across different aneurysm subregions defined by saccular, streamwise, and topographical divisions. The results revealed significant variations in these metrics, correlating distinct hemodynamic environments with wall features on the aneurysm walls, such as bleb formation. Critical findings indicated that regions with low WSS and high OSI, particularly in the body and central regions of aneurysms, are prone to conditions that promote bleb formation. Conversely, areas exposed to high WSS and positive divergence, like the aneurysm neck, inflow, and outflow regions, exhibited a different but substantial risk profile for bleb development, influenced by flow impingements and convergences. These insights highlight the complexity of aneurysm behavior, suggesting that both high and low-shear environments can contribute to aneurysm pathology through distinct mechanisms.</p>","PeriodicalId":50349,"journal":{"name":"International Journal for Numerical Methods in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11315625/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141477902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The aortic valve (AV) is crucial for cardiovascular (CV) hemodynamic, impacting cardiac output (CO) and left ventricular volumetric flow rate (LVQ). Its nonlinear behavior challenges standard LVQ prediction methods as well as CO one. This study presents a novel approach for modeling the AV in the CV system, offering an improved method for estimating crucial parameters like LVQ across various AV conditions, including aortic stenosis (AS). The model, based on AV channel length during the entire cardiac phase, introduces a time-varying AV resistance (TV-AVR) parameterized by the pressure ratio across the AV and LVQ, enabling the simulation of both healthy and AS-related conditions. To validate this model, in vitro measurements are compared using a hybrid mock circulatory loop device. An unconventional use of a convolutional neural network (CNN) corrects the model's estimates, eliminating the need for labeled datasets. This approach, incorporating real-time learning and transforming 1-D CV signals into 2-D tensors, significantly improves the accuracy of LVQ measurements, achieving an error rate of less than 3.41 ± 4.84% for CO in healthy conditions and 2.83 ± 1.35% in AS cases—a 33.13% enhancement over linear diode models. These results underscore the potential of this approach for enhancing the diagnosis, prediction, and treatment of AV diseases. The key contributions of the proposed method encompass nonlinear TV-AVR estimation, investigation of transient CV responses, prediction of instantaneous CO, development of a flexible framework for noninvasive measurements integration, and the introduction of an adjustable resistance model using an extended Kalman filter (EKF) and CNN combination, all without requiring labeled data.
主动脉瓣(AV)对心血管(CV)血流动力学至关重要,影响心输出量(CO)和左心室容积流量(LVQ)。它的非线性行为对标准 LVQ 预测方法和 CO 预测方法提出了挑战。本研究提出了一种对心血管系统中的房室进行建模的新方法,为估算包括主动脉瓣狭窄(AS)在内的各种房室情况下的 LVQ 等关键参数提供了一种改进方法。该模型以整个心动期的房室通道长度为基础,引入了时变房室阻力(TV-AVR),参数为房室压力比和 LVQ,可模拟健康和 AS 相关情况。为了验证该模型,我们使用混合模拟循环回路装置对体外测量结果进行了比较。卷积神经网络(CNN)的非常规使用修正了模型的估计值,从而消除了对标记数据集的需求。这种方法结合了实时学习,并将一维 CV 信号转化为二维张量,显著提高了 LVQ 测量的准确性,在健康状况下 CO 的误差率低于 3.41 ± 4.84%,在 AS 病例中误差率为 2.83 ± 1.35%,比线性二极管模型提高了 33.13%。这些结果凸显了这种方法在提高诊断、预测和治疗视网膜疾病方面的潜力。所提方法的主要贡献包括非线性 TV-AVR 估计、研究瞬时 CV 响应、预测瞬时 CO、开发无创测量集成的灵活框架,以及使用扩展卡尔曼滤波器 (EKF) 和 CNN 组合引入可调电阻模型,所有这些都不需要标记数据。
{"title":"An intelligent aortic valve model for complete cardiac cycle","authors":"Mehmet Iscan, Aydin Yesildirek","doi":"10.1002/cnm.3838","DOIUrl":"10.1002/cnm.3838","url":null,"abstract":"<p>The aortic valve (AV) is crucial for cardiovascular (CV) hemodynamic, impacting cardiac output (CO) and left ventricular volumetric flow rate (LVQ). Its nonlinear behavior challenges standard LVQ prediction methods as well as CO one. This study presents a novel approach for modeling the AV in the CV system, offering an improved method for estimating crucial parameters like LVQ across various AV conditions, including aortic stenosis (AS). The model, based on AV channel length during the entire cardiac phase, introduces a time-varying AV resistance (TV-AVR) parameterized by the pressure ratio across the AV and LVQ, enabling the simulation of both healthy and AS-related conditions. To validate this model, in vitro measurements are compared using a hybrid mock circulatory loop device. An unconventional use of a convolutional neural network (CNN) corrects the model's estimates, eliminating the need for labeled datasets. This approach, incorporating real-time learning and transforming 1-D CV signals into 2-D tensors, significantly improves the accuracy of LVQ measurements, achieving an error rate of less than 3.41 ± 4.84% for CO in healthy conditions and 2.83 ± 1.35% in AS cases—a 33.13% enhancement over linear diode models. These results underscore the potential of this approach for enhancing the diagnosis, prediction, and treatment of AV diseases. The key contributions of the proposed method encompass nonlinear TV-AVR estimation, investigation of transient CV responses, prediction of instantaneous CO, development of a flexible framework for noninvasive measurements integration, and the introduction of an adjustable resistance model using an extended Kalman filter (EKF) and CNN combination, all without requiring labeled data.</p>","PeriodicalId":50349,"journal":{"name":"International Journal for Numerical Methods in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cnm.3838","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141421712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jyoti, Soobin Kwak, Seokjun Ham, Youngjin Hwang, Seungyoon Kang, Junseok Kim
This study aims to investigate how inert gas affects the partial pressure of alveolar and venous blood using a fast and accurate operator splitting method (OSM). Unlike previous complex methods, such as the finite element method (FEM), OSM effectively separates governing equations into smaller sub-problems, facilitating a better understanding of inert gas transport and exchange between blood capillaries and surrounding tissue. The governing equations were discretized with a fully implicit finite difference method (FDM), which enables the use of larger time steps. The model employed partial differential equations, considering convection-diffusion in blood and only diffusion in tissue. The study explores the impact of initial arterial pressure, breathing frequency, blood flow velocity, solubility, and diffusivity on the partial pressure of inert gas in blood and tissue. Additionally, the effects of anesthetic inert gas and oxygen on venous blood partial pressure were analyzed. Simulation results demonstrate that the high solubility and diffusivity of anesthetic inert gas lead to its prolonged presence in blood and tissue, resulting in lower partial pressure in venous blood. These findings enhance our understanding of inert gas interaction with alveolar/venous blood, with potential implications for medical diagnostics and therapies.
{"title":"Analysis of the effect of inert gas on alveolar/venous blood partial pressure by using the operator splitting method","authors":"Jyoti, Soobin Kwak, Seokjun Ham, Youngjin Hwang, Seungyoon Kang, Junseok Kim","doi":"10.1002/cnm.3839","DOIUrl":"10.1002/cnm.3839","url":null,"abstract":"<p>This study aims to investigate how inert gas affects the partial pressure of alveolar and venous blood using a fast and accurate operator splitting method (OSM). Unlike previous complex methods, such as the finite element method (FEM), OSM effectively separates governing equations into smaller sub-problems, facilitating a better understanding of inert gas transport and exchange between blood capillaries and surrounding tissue. The governing equations were discretized with a fully implicit finite difference method (FDM), which enables the use of larger time steps. The model employed partial differential equations, considering convection-diffusion in blood and only diffusion in tissue. The study explores the impact of initial arterial pressure, breathing frequency, blood flow velocity, solubility, and diffusivity on the partial pressure of inert gas in blood and tissue. Additionally, the effects of anesthetic inert gas and oxygen on venous blood partial pressure were analyzed. Simulation results demonstrate that the high solubility and diffusivity of anesthetic inert gas lead to its prolonged presence in blood and tissue, resulting in lower partial pressure in venous blood. These findings enhance our understanding of inert gas interaction with alveolar/venous blood, with potential implications for medical diagnostics and therapies.</p>","PeriodicalId":50349,"journal":{"name":"International Journal for Numerical Methods in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141421713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A high failure rate is associated with fracture plates in proximal humerus fractures. The causes of failure remain unclear due to the complexity of the problem including the number and position of the screws, their length and orientation in the space. Finite element (FE) analysis has been used for the analysis of plating of proximal humeral fractures, but due to computational costs is unable to fully explore all potential screw combinations. Surrogate modelling is a viable solution, having the potential to significantly reduce the computational cost whilst requiring a moderate number of training sets. This study aimed to develop adaptive neural network (ANN)-based surrogate models to predict the strain in the humeral bone as a result of changing the length of the screws. The ANN models were trained using data from FE simulations of a single humerus, and after defining the best training sample size, multiple and single-output models were developed. The best performing ANN model was used to predict all the possible screw length configurations. The ANN predictions were compared with the FE results of unseen data, showing a good correlation (R2 = 0.99) and low levels of error (RMSE = 0.51%–1.83% strain). The ANN predictions of all possible screw length configurations showed that the screw that provided the medial support was the most influential on the predicted strain. Overall, the ANN-based surrogate model accurately captured bone strains and has the potential to be used for more complex problems with a larger number of variables.
在肱骨近端骨折中,骨折钢板的失败率很高。由于问题的复杂性(包括螺钉的数量和位置、长度以及在空间中的方向),失败的原因仍不清楚。有限元(FE)分析已被用于分析肱骨近端骨折的钢板,但由于计算成本的原因,无法充分探索所有潜在的螺钉组合。代用模型是一种可行的解决方案,有可能显著降低计算成本,同时只需要适量的训练集。本研究旨在开发基于自适应神经网络(ANN)的代用模型,以预测改变螺钉长度后肱骨中的应变。使用单个肱骨的有限元模拟数据对自适应神经网络模型进行了训练,在确定最佳训练样本大小后,开发了多输出和单输出模型。性能最好的 ANN 模型用于预测所有可能的螺钉长度配置。将 ANN 预测结果与未见数据的 FE 结果进行比较,结果显示相关性良好(R2 = 0.99),误差较小(RMSE = 0.51%-1.83% 应变)。对所有可能的螺钉长度配置进行的 ANN 预测表明,提供内侧支撑的螺钉对预测应变的影响最大。总之,基于 ANN 的代用模型准确地捕捉到了骨应变,并有可能用于变量较多的更复杂问题。
{"title":"Assessing screw length impact on bone strain in proximal humerus fracture fixation via surrogate modelling","authors":"Daniela Mini, Karen J. Reynolds, Mark Taylor","doi":"10.1002/cnm.3840","DOIUrl":"10.1002/cnm.3840","url":null,"abstract":"<p>A high failure rate is associated with fracture plates in proximal humerus fractures. The causes of failure remain unclear due to the complexity of the problem including the number and position of the screws, their length and orientation in the space. Finite element (FE) analysis has been used for the analysis of plating of proximal humeral fractures, but due to computational costs is unable to fully explore all potential screw combinations. Surrogate modelling is a viable solution, having the potential to significantly reduce the computational cost whilst requiring a moderate number of training sets. This study aimed to develop adaptive neural network (ANN)-based surrogate models to predict the strain in the humeral bone as a result of changing the length of the screws. The ANN models were trained using data from FE simulations of a single humerus, and after defining the best training sample size, multiple and single-output models were developed. The best performing ANN model was used to predict all the possible screw length configurations. The ANN predictions were compared with the FE results of unseen data, showing a good correlation (<i>R</i><sup>2</sup> = 0.99) and low levels of error (RMSE = 0.51%–1.83% strain). The ANN predictions of all possible screw length configurations showed that the screw that provided the medial support was the most influential on the predicted strain. Overall, the ANN-based surrogate model accurately captured bone strains and has the potential to be used for more complex problems with a larger number of variables.</p>","PeriodicalId":50349,"journal":{"name":"International Journal for Numerical Methods in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cnm.3840","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141312191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yogesh Karnam, Fernando Mut, Alexander K. Yu, Boyle Cheng, Sepideh Amin-Hanjani, Fady T. Charbel, Henry H. Woo, Mika Niemelä, Riikka Tulamo, Behnam Rezai Jahromi, Juhana Frösen, Yasutaka Tobe, Anne M. Robertson, Juan R. Cebral
The mechanisms behind intracranial aneurysm formation and rupture are not fully understood, with factors such as location, patient demographics, and hemodynamics playing a role. Additionally, the significance of anatomical features like blebs in ruptures is debated. This highlights the necessity for comprehensive research that combines patient-specific risk factors with a detailed analysis of local hemodynamic characteristics at bleb and rupture sites. Our study analyzed 359 intracranial aneurysms from 268 patients, reconstructing patient-specific models for hemodynamic simulations based on 3D rotational angiographic images and intraoperative videos. We identified aneurysm subregions and delineated rupture sites, characterizing blebs and their regional overlap, employing statistical comparisons across demographics, and other risk factors. This work identifies patterns in aneurysm rupture sites, predominantly at the dome, with variations across patient demographics. Hypertensive and anterior communicating artery (ACom) aneurysms showed specific rupture patterns and bleb associations, indicating two pathways: high-flow in ACom with thin blebs at impingement sites and low-flow, oscillatory conditions in middle cerebral artery (MCA) aneurysms fostering thick blebs. Bleb characteristics varied with gender, age, and smoking, linking rupture risks to hemodynamic factors and patient profiles. These insights enhance understanding of the hemodynamic mechanisms leading to rupture events. This analysis elucidates the role of localized hemodynamics in intracranial aneurysm rupture, challenging the emphasis on location by revealing how flow variations influence stability and risk. We identify two pathways to wall failure—high-flow and low-flow conditions—highlighting the complexity of aneurysm behavior. Additionally, this research advances our knowledge of how inherent patient-specific characteristics impact these processes, which need further investigation.
{"title":"Distribution of rupture sites and blebs on intracranial aneurysm walls suggests distinct rupture patterns in ACom and MCA aneurysms","authors":"Yogesh Karnam, Fernando Mut, Alexander K. Yu, Boyle Cheng, Sepideh Amin-Hanjani, Fady T. Charbel, Henry H. Woo, Mika Niemelä, Riikka Tulamo, Behnam Rezai Jahromi, Juhana Frösen, Yasutaka Tobe, Anne M. Robertson, Juan R. Cebral","doi":"10.1002/cnm.3837","DOIUrl":"10.1002/cnm.3837","url":null,"abstract":"<p>The mechanisms behind intracranial aneurysm formation and rupture are not fully understood, with factors such as location, patient demographics, and hemodynamics playing a role. Additionally, the significance of anatomical features like blebs in ruptures is debated. This highlights the necessity for comprehensive research that combines patient-specific risk factors with a detailed analysis of local hemodynamic characteristics at bleb and rupture sites. Our study analyzed 359 intracranial aneurysms from 268 patients, reconstructing patient-specific models for hemodynamic simulations based on 3D rotational angiographic images and intraoperative videos. We identified aneurysm subregions and delineated rupture sites, characterizing blebs and their regional overlap, employing statistical comparisons across demographics, and other risk factors. This work identifies patterns in aneurysm rupture sites, predominantly at the dome, with variations across patient demographics. Hypertensive and anterior communicating artery (ACom) aneurysms showed specific rupture patterns and bleb associations, indicating two pathways: high-flow in ACom with thin blebs at impingement sites and low-flow, oscillatory conditions in middle cerebral artery (MCA) aneurysms fostering thick blebs. Bleb characteristics varied with gender, age, and smoking, linking rupture risks to hemodynamic factors and patient profiles. These insights enhance understanding of the hemodynamic mechanisms leading to rupture events. This analysis elucidates the role of localized hemodynamics in intracranial aneurysm rupture, challenging the emphasis on location by revealing how flow variations influence stability and risk. We identify two pathways to wall failure—high-flow and low-flow conditions—highlighting the complexity of aneurysm behavior. Additionally, this research advances our knowledge of how inherent patient-specific characteristics impact these processes, which need further investigation.</p>","PeriodicalId":50349,"journal":{"name":"International Journal for Numerical Methods in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11315635/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141263279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Friederike Schäfer, Daniele E. Schiavazzi, Leif Rune Hellevik, Jacob Sturdy
Computational models of the cardiovascular system are increasingly used for the diagnosis, treatment, and prevention of cardiovascular disease. Before being used for translational applications, the predictive abilities of these models need to be thoroughly demonstrated through verification, validation, and uncertainty quantification. When results depend on multiple uncertain inputs, sensitivity analysis is typically the first step required to separate relevant from unimportant inputs, and is key to determine an initial reduction on the problem dimensionality that will significantly affect the cost of all downstream analysis tasks. For computationally expensive models with numerous uncertain inputs, sample-based sensitivity analysis may become impractical due to the substantial number of model evaluations it typically necessitates. To overcome this limitation, we consider recently proposed Multifidelity Monte Carlo estimators for Sobol’ sensitivity indices, and demonstrate their applicability to an idealized model of the common carotid artery. Variance reduction is achieved combining a small number of three-dimensional fluid–structure interaction simulations with affordable one- and zero-dimensional reduced-order models. These multifidelity Monte Carlo estimators are compared with traditional Monte Carlo and polynomial chaos expansion estimates. Specifically, we show consistent sensitivity ranks for both bi- (1D/0D) and tri-fidelity (3D/1D/0D) estimators, and superior variance reduction compared to traditional single-fidelity Monte Carlo estimators for the same computational budget. As the computational burden of Monte Carlo estimators for Sobol’ indices is significantly affected by the problem dimensionality, polynomial chaos expansion is found to have lower computational cost for idealized models with smooth stochastic response.
{"title":"Global sensitivity analysis with multifidelity Monte Carlo and polynomial chaos expansion for vascular haemodynamics","authors":"Friederike Schäfer, Daniele E. Schiavazzi, Leif Rune Hellevik, Jacob Sturdy","doi":"10.1002/cnm.3836","DOIUrl":"10.1002/cnm.3836","url":null,"abstract":"<p>Computational models of the cardiovascular system are increasingly used for the diagnosis, treatment, and prevention of cardiovascular disease. Before being used for translational applications, the predictive abilities of these models need to be thoroughly demonstrated through verification, validation, and uncertainty quantification. When results depend on multiple uncertain inputs, sensitivity analysis is typically the first step required to separate relevant from unimportant inputs, and is key to determine an initial reduction on the problem dimensionality that will significantly affect the cost of all downstream analysis tasks. For computationally expensive models with numerous uncertain inputs, sample-based sensitivity analysis may become impractical due to the substantial number of model evaluations it typically necessitates. To overcome this limitation, we consider recently proposed Multifidelity Monte Carlo estimators for Sobol’ sensitivity indices, and demonstrate their applicability to an idealized model of the common carotid artery. Variance reduction is achieved combining a small number of three-dimensional fluid–structure interaction simulations with affordable one- and zero-dimensional reduced-order models. These multifidelity Monte Carlo estimators are compared with traditional Monte Carlo and polynomial chaos expansion estimates. Specifically, we show consistent sensitivity ranks for both bi- (1D/0D) and tri-fidelity (3D/1D/0D) estimators, and superior variance reduction compared to traditional single-fidelity Monte Carlo estimators for the same computational budget. As the computational burden of Monte Carlo estimators for Sobol’ indices is significantly affected by the problem dimensionality, polynomial chaos expansion is found to have lower computational cost for idealized models with smooth stochastic response.</p>","PeriodicalId":50349,"journal":{"name":"International Journal for Numerical Methods in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141263358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Microwave ablation has become a viable alternative for cancer treatment for patients who cannot undergo surgery. During this procedure, a single-slot coaxial antenna is employed to effectively deliver microwave energy to the targeted tissue. The success of the treatment was measured by the amount of ablation zone created during the ablation procedure. The significantly large blood vessel placed near the antenna causes heat dissipation by convection around the blood vessel. The heat sink effect could result in insufficient ablation, raising the risk of local tumor recurrence. In this study, we investigated the heat loss due to large blood vessels and the relationship between blood velocity and temperature distribution. The hepatic artery, with a diameter of 4 mm and a height of 50 mm and two branches, is considered in the computational domain. The temperature profile, localized tissue contraction, and ablation zones were simulated for initial blood velocities 0.05, 0.1, and 0.16 m/s using the 3D Pennes bio-heat equation, temperature–time dependent model, and cell death model, respectively. Temperature-dependent blood velocity is modeled using the Navier–Stokes equation, and the fluid–solid interaction boundary is treated as a convective boundary. For discretization, we utilized