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Description of the local hemodynamic environment in intracranial aneurysm wall subdivisions 描述颅内动脉瘤壁分支的局部血液动力学环境。
IF 2.2 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-07-01 DOI: 10.1002/cnm.3844
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.

颅内动脉瘤(IAs)受血液动力学的影响,对健康构成严重威胁。本研究的重点是分析动脉瘤壁内血流动力学状况的复杂特征及其对出血点发展的影响,旨在加深对动脉瘤稳定性和破裂风险的理解。该方法强调利用来自 268 名患者的 359 个内腔瘤和 213 个内腔瘤出血点的综合数据集重建患者特定的血管模型,并使用有限元方法分析血流,以求解非稳态纳维-斯托克斯方程、对动脉瘤壁亚区进行分割,并计算和分析按囊状、流状和地形分割的不同动脉瘤亚区的血流动力学指标壁剪应力(WSS)、其指标和 WSS 场临界点。结果显示,这些指标存在明显差异,不同的血流动力学环境与动脉瘤壁的特征(如瘤栓形成)相关联。关键的研究结果表明,低 WSS 和高 OSI 的区域,尤其是动脉瘤体和中央区域,容易形成促进瘤栓形成的条件。相反,高 WSS 和正发散区域,如动脉瘤颈部、流入和流出区域,受血流阻塞和汇聚的影响,则表现出不同的血泡形成风险,但风险很大。这些见解凸显了动脉瘤行为的复杂性,表明高剪切和低剪切环境可通过不同的机制导致动脉瘤病变。
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引用次数: 0
An intelligent aortic valve model for complete cardiac cycle 用于完整心脏循环的智能主动脉瓣模型。
IF 2.2 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-06-18 DOI: 10.1002/cnm.3838
Mehmet Iscan, Aydin Yesildirek

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。
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引用次数: 0
Analysis of the effect of inert gas on alveolar/venous blood partial pressure by using the operator splitting method 利用算子分割法分析惰性气体对肺泡/静脉血分压的影响。
IF 2.2 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-06-17 DOI: 10.1002/cnm.3839
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.

本研究旨在利用快速准确的算子分割法(OSM)研究惰性气体如何影响肺泡和静脉血的分压。与有限元法(FEM)等以往的复杂方法不同,OSM 能有效地将控制方程分离成更小的子问题,从而有助于更好地理解惰性气体在毛细血管和周围组织之间的传输和交换。采用全隐式有限差分法(FDM)对控制方程进行离散化,从而可以使用更大的时间步长。该模型采用偏微分方程,考虑了血液中的对流-扩散和组织中的扩散。研究探讨了初始动脉压、呼吸频率、血流速度、溶解度和扩散率对血液和组织中惰性气体分压的影响。此外,还分析了麻醉惰性气体和氧气对静脉血分压的影响。模拟结果表明,麻醉惰性气体的高溶解性和高扩散性导致其在血液和组织中长期存在,从而降低了静脉血中的分压。这些发现加深了我们对惰性气体与肺泡/静脉血相互作用的理解,对医学诊断和治疗具有潜在的影响。
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引用次数: 0
Assessing screw length impact on bone strain in proximal humerus fracture fixation via surrogate modelling 通过替代模型评估螺钉长度对肱骨近端骨折固定中骨应变的影响。
IF 2.2 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-06-12 DOI: 10.1002/cnm.3840
Daniela Mini, Karen J. Reynolds, Mark Taylor

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。
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引用次数: 0
Distribution of rupture sites and blebs on intracranial aneurysm walls suggests distinct rupture patterns in ACom and MCA aneurysms 颅内动脉瘤壁上破裂点和出血点的分布表明 ACom 和 MCA 动脉瘤的破裂模式截然不同。
IF 2.2 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-06-05 DOI: 10.1002/cnm.3837
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.

颅内动脉瘤形成和破裂背后的机制尚未完全明了,位置、患者人口统计学和血液动力学等因素都起着作用。此外,对出血点等解剖学特征在破裂中的意义也存在争议。这凸显了将患者特异性风险因素与详细分析出血点和破裂部位的局部血流动力学特征相结合进行综合研究的必要性。我们的研究分析了 268 名患者的 359 个颅内动脉瘤,根据三维旋转血管造影图像和术中视频重建了患者特异性模型,用于血流动力学模拟。我们确定了动脉瘤亚区并划定了破裂部位,描述了出血点及其区域重叠情况,采用了跨人口统计学和其他风险因素的统计比较。这项工作确定了动脉瘤破裂部位的模式,主要是在穹顶处,不同患者的人口统计学特征存在差异。高血压动脉瘤和前交通动脉(ACom)动脉瘤显示出特定的破裂模式和出血点关联,表明有两种途径:ACom动脉瘤中的高流量和撞击部位的薄出血点,以及大脑中动脉(MCA)动脉瘤中的低流量、振荡条件和厚出血点。出血点特征因性别、年龄和吸烟而异,这将破裂风险与血液动力学因素和患者特征联系起来。这些见解加深了人们对导致破裂事件的血液动力学机制的理解。这项分析阐明了局部血流动力学在颅内动脉瘤破裂中的作用,通过揭示血流变化如何影响稳定性和风险,对强调位置的观点提出了挑战。我们确定了动脉瘤壁破裂的两种途径--高流量和低流量条件--凸显了动脉瘤行为的复杂性。此外,这项研究还增进了我们对患者固有的特异性特征如何影响这些过程的了解,这些过程还需要进一步研究。
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引用次数: 0
Global sensitivity analysis with multifidelity Monte Carlo and polynomial chaos expansion for vascular haemodynamics 利用多保真蒙特卡洛和多项式混沌扩展对血管血流动力学进行全局敏感性分析。
IF 2.2 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-06-05 DOI: 10.1002/cnm.3836
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.

心血管系统的计算模型越来越多地用于心血管疾病的诊断、治疗和预防。在用于转化应用之前,需要通过验证、确认和不确定性量化来彻底证明这些模型的预测能力。当结果取决于多个不确定输入时,灵敏度分析通常是区分相关输入和不重要输入所需的第一步,也是确定初步降低问题维度的关键,这将显著影响所有下游分析任务的成本。对于具有大量不确定输入的计算昂贵的模型,基于样本的灵敏度分析可能会变得不切实际,因为它通常需要对模型进行大量评估。为了克服这一限制,我们考虑了最近提出的 Sobol 敏感性指数多保真度蒙特卡罗估计器,并演示了其在颈总动脉理想化模型中的适用性。通过将少量三维流体-结构相互作用模拟与经济实惠的一维和零维降阶模型相结合,实现了方差的降低。我们将这些多保真度蒙特卡洛估计器与传统的蒙特卡洛估计器和多项式混沌扩展估计器进行了比较。具体而言,我们展示了双保真(1D/0D)和三保真(3D/1D/0D)估计器一致的灵敏度排名,以及在相同计算预算下与传统单保真蒙特卡罗估计器相比更优越的方差缩小。由于 Sobol'指数的蒙特卡罗估计器的计算负担受问题维度的影响很大,因此对于具有平滑随机响应的理想化模型,多项式混沌扩展的计算成本较低。
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引用次数: 0
Effect of high blood flow on heat distribution and ablation zone during microwave ablation-numerical approach 高血流量对微波消融过程中热量分布和消融区的影响--数值方法。
IF 2.2 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-05-27 DOI: 10.1002/cnm.3835
Gangadhara Boregowda, Panchatcharam Mariappan

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 HcurlΩ elements for the wave propagation model, H1Ω elements for the Pennes bio-heat model, and H1Ω3×L02Ω elements for the Navier–Stokes equation, where Ω represents the computational domain. The simulated results show that blood vessels and blood velocity have a significant impact on temperature distribution, tissue contraction, and the volume of the ablation zone.

微波消融术已成为无法接受手术治疗的癌症患者的一种可行的替代治疗方法。在这种治疗过程中,采用单槽同轴电缆将微波能量有效地传送到目标组织。治疗的成功与否取决于消融过程中形成的消融区的大小。天线附近的血管非常大,会通过血管周围的对流造成散热。这种散热效应可能导致消融不充分,增加局部肿瘤复发的风险。在这项研究中,我们研究了大血管造成的热损失以及血流速度和温度分布之间的关系。在计算域中考虑了直径为 4 毫米、高 50 毫米并有两个分支的肝动脉。在初始血流速度为 0.05、0.1 和 0.16 m/s 时,分别使用三维 Pennes 生物热方程、温度-时间相关模型和细胞死亡模型模拟了温度分布、局部组织收缩和消融区。随温度变化的血流速度采用纳维-斯托克斯方程建模,流固相互作用边界被视为对流边界。在离散化方面,我们使用 H curl Ω $$ Hleft(operatorname{curl},Omega right) $$ 元素来建立波传播模型,使用 H 1 Ω $$ {H}^1left(Omega right) $$ 元素来建立彭尼斯生物热模型、and H 1 Ω 3 × L 0 2 Ω $$ {left({H}^1left(Omega right)right)}^3times {L}_0^2left(Omega right) $$ elements for the Navier-Stokes equation, where Ω $$ Omega $$ represents the computational domain.模拟结果表明,血管和血流速度对温度分布、组织收缩和消融区体积有显著影响。
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引用次数: 0
A three-dimensional, discrete-continuum model of blood pressure in microvascular networks 微血管网络中血压的三维离散连续模型。
IF 2.2 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-05-21 DOI: 10.1002/cnm.3832
Paul W. Sweeney, Claire Walsh, Simon Walker-Samuel, Rebecca J. Shipley

We present a 3D discrete-continuum model to simulate blood pressure in large microvascular tissues in the absence of known capillary network architecture. Our hybrid approach combines a 1D Poiseuille flow description for large, discrete arteriolar and venular networks coupled to a continuum-based Darcy model, point sources of flux, for transport in the capillary bed. We evaluate our hybrid approach using a vascular network imaged from the mouse brain medulla/pons using multi-fluorescence high-resolution episcopic microscopy (MF-HREM). We use the fully-resolved vascular network to predict the hydraulic conductivity of the capillary network and generate a fully-discrete pressure solution to benchmark against. Our results demonstrate that the discrete-continuum methodology is a computationally feasible and effective tool for predicting blood pressure in real-world microvascular tissues when capillary microvessels are poorly defined.

我们提出了一种三维离散-连续模型,用于在没有已知毛细血管网络结构的情况下模拟大型微血管组织中的血压。我们的混合方法结合了对大型离散动脉和静脉网络的一维普瓦赛流描述,以及基于连续体的达西模型(点通量源),用于毛细血管床的传输。我们使用多重荧光高分辨率外显微镜(MF-HREM)对小鼠大脑髓质/大脑皮质的血管网络成像进行了评估。我们使用完全解析的血管网络预测毛细血管网络的水力传导性,并生成完全离散的压力解决方案作为基准。我们的研究结果表明,当毛细管微血管定义不清时,离散连续方法是预测真实世界微血管组织血压的一种计算上可行且有效的工具。
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引用次数: 0
A one-dimensional computational model for blood flow in an elastic blood vessel with a rigid catheter 带有刚性导管的弹性血管中血流的一维计算模型。
IF 2.2 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-05-12 DOI: 10.1002/cnm.3834
Aseem Milind Pradhan, Fernando Mut, Juan Raul Cebral

Strokes are one of the leading causes of death in the United States. Stroke treatment involves removal or dissolution of the obstruction (usually a clot) in the blocked artery by catheter insertion. A computer simulation to systematically plan such patient-specific treatments needs a network of about 105 blood vessels including collaterals. The existing computational fluid dynamic (CFD) solvers are not employed for stroke treatment planning as they are incapable of providing solutions for such big arterial trees in a reasonable amount of time. This work presents a novel one-dimensional mathematical formulation for blood flow modeling in an elastic blood vessel with a centrally placed rigid catheter. The governing equations are first-order hyperbolic partial differential equations, and the hypergeometric function needs to be computed to obtain the characteristic system of these hyperbolic equations. We employed the Discontinuous Galerkin method to solve the hyperbolic system and validated the implementation by comparing it against a well-established 3D CFD solver using idealized vessels and a realistic truncated arterial network. The results showed clinically insignificant differences in steady flow cases, with overall variations between 1D and 3D models remaining below 10%. Additionally, the solver accurately captured wave reflection phenomena at domain discontinuities in unsteady cases. A primary advantage of this model over 3D solvers is its ease in obtaining a discretized geometry of complex vasculatures with multiple arterial branches. Thus, the 1D computational model offers good accuracy and applicability in simulating complex vasculatures, demonstrating promising potential for investigating patient-specific endovascular interventions in strokes.

中风是导致美国人死亡的主要原因之一。脑卒中治疗包括通过插入导管清除或溶解阻塞动脉中的阻塞物(通常是血栓)。要系统地规划这种针对病人的治疗方法,计算机模拟需要一个由包括络脉在内的约 105 条血管组成的网络。现有的计算流体动力学(CFD)求解器无法在合理的时间内为如此庞大的动脉树提供解决方案,因此无法用于中风治疗规划。本研究提出了一种新颖的一维数学公式,用于对带有中心放置的刚性导管的弹性血管中的血流进行建模。控制方程为一阶双曲偏微分方程,需要计算双曲函数以获得这些双曲方程的特征系统。我们采用了非连续 Galerkin 法来求解双曲系统,并将其与使用理想化血管和现实截断动脉网络的成熟 3D CFD 求解器进行了比较,从而验证了该方法的实施效果。结果显示,在稳定流情况下,临床上的差异并不明显,一维模型和三维模型之间的总体差异保持在 10%以下。此外,该求解器还准确捕捉到了非稳态情况下域不连续处的波反射现象。与三维求解器相比,该模型的一个主要优势是易于获得具有多个动脉分支的复杂血管的离散几何形状。因此,一维计算模型在模拟复杂血管方面具有良好的准确性和适用性,在研究特定患者的脑卒中血管内介入方面具有广阔的前景。
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引用次数: 0
Hybrid deep learning assisted multi classification: Grading of malignant thyroid nodules 混合深度学习辅助多重分类:恶性甲状腺结节分级
IF 2.2 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-05-12 DOI: 10.1002/cnm.3824
Mayuresh Bhagavat Gulame, Vaibhav V. Dixit

Thyroid nodules are commonly diagnosed with ultrasonography, which includes internal characteristics, varying looks, and hazy boundaries, making it challenging for a clinician to differentiate between malignant and benign forms based only on visual identification. The advancement of AI, particularly DL, provides significant breakthroughs in the domain of medical image identification. Yet, there are certain obstacles to achieving accuracy as well as efficacy in thyroid nodule detection. The thyroid nodules in this study are detected and classified using an inventive hybrid deep learning-assisted multi-classification method. The median blur method is applied in this work to eliminate the salt and pepper noise from the image. Then MPIU-Net-based segmentation is utilized to segment the image. The LGBPNP-based features are retrieved from the segmented image to obtain a single histogram sequence of the LGBP pattern in addition to other features like extraction of multi-texton and LTP-based features. After the feature extraction, the data augmentation process is applied and then the features are fed to the hybrid classification-based nodule classification model that comprises Deep Maxout and CNN, this hybrid classification trains the features and predicts the thyroid nodule. Additionally, the TIRADS score classification is used for the projected malignant thyroid nodule coupled with statistical features collected from the segmented. The DBNAAF with transfer learning model is employed to classify the grading of malignant thyroid nodules, where the weights of the model are learned with transfer learning. The MCC of the Hybrid Model is 0.9445, whereas the DCNN is 0.6858, YOLOV3-DMRF is 0.7229, CNN is 0.7780, DBN is 0.7601, Bi-GRU is 0.7038, Deep Maxout is 0.7528, and RNN is 0.8522, respectively.

甲状腺结节通常通过超声波诊断,其内部特征、外观各异、边界模糊,这使得临床医生仅凭视觉识别来区分恶性和良性结节极具挑战性。人工智能尤其是 DL 的发展为医学图像识别领域带来了重大突破。然而,要实现甲状腺结节检测的准确性和有效性还存在一定的障碍。本研究采用一种创造性的混合深度学习辅助多分类方法对甲状腺结节进行检测和分类。本研究采用中值模糊法消除图像中的椒盐噪声。然后利用基于 MPIU-Net 的分割法对图像进行分割。从分割后的图像中提取基于 LGBPNP 的特征,以获得 LGBP 模式的单一直方图序列,以及其他特征,如提取基于多文本和 LTP 的特征。提取特征后,应用数据增强过程,然后将特征输入由 Deep Maxout 和 CNN 组成的基于混合分类的结节分类模型,该混合分类模型训练特征并预测甲状腺结节。此外,TIRADS 评分分类与从分割中收集的统计特征相结合,用于预测恶性甲状腺结节。采用带有迁移学习模型的 DBNAAF 对恶性甲状腺结节进行分级,模型的权重是通过迁移学习获得的。混合模型的 MCC 为 0.9445,而 DCNN 为 0.6858,YOLOV3-DMRF 为 0.7229,CNN 为 0.7780,DBN 为 0.7601,Bi-GRU 为 0.7038,Deep Maxout 为 0.7528,RNN 为 0.8522。
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引用次数: 0
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International Journal for Numerical Methods in Biomedical Engineering
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