基于超声多普勒的腹主动脉个性化血液动力学建模的可行性。

IF 2.9 4区 医学 Q3 ENGINEERING, BIOMEDICAL
Judith Fonken, Milan Gillissen, Eline van Engelen, Marc van Sambeek, Frans van de Vosse, Richard Lopata
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引用次数: 0

摘要

背景:个性化建模是改善腹主动脉瘤(AAA)破裂风险评估的一种有前途的工具。计算机断层扫描(CT)和定量血流(Q-flow)磁共振成像(MRI)分别被广泛认为是获取患者特异性几何形状和速度曲线的黄金标准。然而,它们的频繁使用受到各种缺陷的阻碍。超声波在目前的临床实践中得到了广泛应用,它是获取患者特异性几何形状和速度曲线的一种安全、快速且经济有效的方法。本研究旨在从多普勒超声中提取并验证患者特异性速度曲线,并研究速度曲线对计算血液动力学的影响:方法:成功获取了六名志愿者和七名患者的脉冲波多普勒(PWD)和彩色多普勒(CD)数据,并分别用于提取横截面上的血流脉冲和速度曲线。将 US 流量脉冲和速度剖面以及通用 Womersley 剖面与 MRI 速度和流量进行了比较。此外,还进行了 CFD 模拟,以检查速度剖面和流动脉冲的综合影响:结果:在横截面上,US 和 MRI 速度剖面之间存在很大差异,US 的差异范围与 Womersley 剖面相同。血流脉冲的差异表明,US 在最大血流、前向血流以及前向血流和后向血流的比率方面通常表现最佳,而它往往高估了后向血流。计算血流动力学的空间模式和大小都受到规定速度边界条件的很大影响。与志愿者相比,在患者身上观察到的 US 和一般 CFD 案例之间的误差更大,差异更小:这些结果表明,从 PWD 数据中获取患者特异性血流脉冲是可行的,前提是 PWD 采集可以在动脉瘤区域近端进行,并产生三相流模式。然而,使用 CD 数据获取横截面上患者特异性速度曲线并不可靠。对于志愿者来说,使用 US 流速曲线而不是通用流速曲线通常会提高性能,而对于患者来说,超过一半的情况都是如此。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the feasibility of ultrasound Doppler-based personalized hemodynamic modeling of the abdominal aorta.

Background: Personalized modeling is a promising tool to improve abdominal aortic aneurysm (AAA) rupture risk assessment. Computed tomography (CT) and quantitative flow (Q-flow) magnetic resonance imaging (MRI) are widely regarded as the gold standard for acquiring patient-specific geometry and velocity profiles, respectively. However, their frequent utilization is hindered by various drawbacks. Ultrasound is used extensively in current clinical practice and offers a safe, rapid and cost-effective method to acquire patient-specific geometries and velocity profiles. This study aims to extract and validate patient-specific velocity profiles from Doppler ultrasound and to examine the impact of the velocity profiles on computed hemodynamics.

Methods: Pulsed-wave Doppler (PWD) and color Doppler (CD) data were successfully obtained for six volunteers and seven patients and employed to extract the flow pulse and velocity profile over the cross-section, respectively. The US flow pulses and velocity profiles as well as generic Womersley profiles were compared to the MRI velocities and flows. Additionally, CFD simulations were performed to examine the combined impact of the velocity profile and flow pulse.

Results: Large discrepancies were found between the US and MRI velocity profiles over the cross-sections, with differences for US in the same range as for the Womersley profile. Differences in flow pulses revealed that US generally performs best in terms of maximum flow, forward flow and ratios between forward and backward flow, whereas it often overestimates the backward flow. Both spatial patterns and magnitude of the computed hemodynamics were considerably affected by the prescribed velocity boundary conditions. Larger errors and smaller differences between the US and generic CFD cases were observed for patients compared to volunteers.

Conclusion: These results show that it is feasible to acquire the patient-specific flow pulse from PWD data, provided that the PWD acquisition could be performed proximal to the aneurysm region, and resulted in a triphasic flow pattern. However, obtaining the patient-specific velocity profile over the cross-section using CD data is not reliable. For the volunteers, utilizing the US flow profile instead of the generic flow profile generally resulted in improved performance, whereas this was the case in more than half of the cases for the patients.

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来源期刊
BioMedical Engineering OnLine
BioMedical Engineering OnLine 工程技术-工程:生物医学
CiteScore
6.70
自引率
2.60%
发文量
79
审稿时长
1 months
期刊介绍: BioMedical Engineering OnLine is an open access, peer-reviewed journal that is dedicated to publishing research in all areas of biomedical engineering. BioMedical Engineering OnLine is aimed at readers and authors throughout the world, with an interest in using tools of the physical and data sciences and techniques in engineering to understand and solve problems in the biological and medical sciences. Topical areas include, but are not limited to: Bioinformatics- Bioinstrumentation- Biomechanics- Biomedical Devices & Instrumentation- Biomedical Signal Processing- Healthcare Information Systems- Human Dynamics- Neural Engineering- Rehabilitation Engineering- Biomaterials- Biomedical Imaging & Image Processing- BioMEMS and On-Chip Devices- Bio-Micro/Nano Technologies- Biomolecular Engineering- Biosensors- Cardiovascular Systems Engineering- Cellular Engineering- Clinical Engineering- Computational Biology- Drug Delivery Technologies- Modeling Methodologies- Nanomaterials and Nanotechnology in Biomedicine- Respiratory Systems Engineering- Robotics in Medicine- Systems and Synthetic Biology- Systems Biology- Telemedicine/Smartphone Applications in Medicine- Therapeutic Systems, Devices and Technologies- Tissue Engineering
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