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Endovascular Implants
Published in Wilmer W Nichols, Michael F O'Rourke, Elazer R Edelman, Charalambos Vlachopoulos, McDonald's Blood Flow in Arteries, 2022
Elazer Edelman, Lambros Athanasiou, Farhad Rikhtegar Nezami
Hemodynamic visualization in stented arteries has made use of dye injection, Doppler velocimetry, ultrasonic measurements, particle image velocimetry and time-resolved digital particle image velocimetry (TRDPIV), among others (Yazdani et al., 2004; Lewis, 2008; Charonko et al., 2009). Generally, measurements were made, in constant or time-dependent flow rates, in a mock artery made of silicone or similar (mainly transparent) material. Flow separation and recirculation were observed near struts, and unique vortical structures were recorded with variable size and residence time at acceleration and deceleration phases of flow wave using visualization techniques (Lewis, 2008; Rouhi et al., 2013). Determining the spatiotemporal map of wall shear stress, as one of most prominent metrics of hemodynamics, requires precise knowledge of blood flow in three dimensions over the cardiac cycle. Available clinical methods of phase-contrast magnetic resonance imaging (PC-MRI) or Doppler ultrasound, as well as benchtop techniques of velocimetry, cannot precisely resolve the stented geometry or map the wall shear stress pattern in a real configuration. Hence computational fluid dynamics (CFD) has been adopted universally by researchers and clinicians as the method of choice to calculate wall shear stress distribution.
Experimental Methods in Cardiovascular Mechanics
Published in Michel R. Labrosse, Cardiovascular Mechanics, 2018
The experimental determination of the velocity field in vivo or in vitro is of paramount importance for the hemodynamic evaluation of heart function and for testing the ability of medical devices to adequately reproduce healthy blood flow conditions. Currently, several techniques can be used to accurately measure whole plane velocity fields in cardiovascular cavities. The focus here will be on the three most widely used techniques: phase-contrast magnetic resonance imaging (MRI), PIV, and echocardiographic particle image velocimetry (echo-PIV).
Bench testing for polymeric bioresorbable scaffolds
Published in Yoshinobu Onuma, Patrick W.J.C. Serruys, Bioresorbable Scaffolds, 2017
John A. Ormiston, Bruce Webber, Janarthanan Sathananthan, Pau Medrano-Gracia, Susann Beier, Mark W.I. Webster
Computational modeling of blood flow is a common tool for investigation of hemodynamic effects in nonstented and stented vessels with idealized or patient specific shapes [22,23]. These computational fluid dynamic (CFD) simulations can provide superior spatial and temporal resolution compared with other currently used modalities. With sufficient computational resources such as supercomputing facilities with high memory and parallel processing capabilities, computational predictions become increasingly used across many scientific fields. The simplifications and assumptions underlying CFD studies must be validated [24]. One method to validate computationally derived flow and stresses is by measuring flow using phase-contrast magnetic resonance imaging in 3D printed up-scaled models with dimensions derived from a coronary atlas [9,22,23].
Extraction of patient-specific boundary conditions from 4D-DSA and their influence on CFD simulations of cerebral aneurysms
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2022
Yuya Uchiyama, Soichiro Fujimura, Hiroyuki Takao, Hiroshi Ono, Keigo Katayama, Takashi Suzuki, Toshihiro Ishibashi, Katharina Otani, Kostadin Karagiozov, Koji Fukudome, Yuichi Murayama, Makoto Yamamoto
Haemodynamics is believed to play an important role in aneurysm prognosis and patient-specific blood flow simulation methodologies are also believed to accurately reproduce haemodynamics. However, most CFD simulation methods for haemodynamics adopt generalised boundary conditions. Such generalised boundary conditions might be insufficient for performing detailed CFD simulations to investigate the clinical events related to aneurysms because the heart rate and blood flow rate are patient specific. CFD simulations can be used to reproduce a haemodynamic environment that is close to reality by applying patient-specific inflow boundary conditions. The data for these boundary conditions can be obtained by performing additional diagnostic examinations such as phase-contrast magnetic resonance imaging or ultrasound velocimetry. However, as these examinations are not part of routine diagnostic protocols, a specific method based on routine diagnostic investigations are required to extract patient-specific inflow boundary conditions. Thus, we focussed on four-dimensional digital subtraction angiography (4D-DSA), a kind of angiography that is used for normal neurovascular imaging. The 4D-DSA is an application that can capture the time-resolved volume images that visualise contrast media from any direction and at any discrete time step (Davis et al. 2013). Thus, we may be able to determine patient-specific inflow boundary conditions if we extract pulse cycle durations and velocities from 4D-DSA data.
Framework for Planning TMVR using 3-D Imaging, In Silico Modeling, and Virtual Reality
Published in Structural Heart, 2020
Keshav Kohli, Zhenglun Alan Wei, Vahid Sadri, Thomas F. Easley, Eric L. Pierce, Yingnan Nancy Zhang, Dee Dee Wang, Adam B. Greenbaum, John C. Lisko, Jaffar M. Khan, Robert J. Lederman, Philipp Blanke, John N. Oshinski, Vasilis Babaliaros, Ajit P. Yoganathan
Computational fluid dynamics (CFD) can be used to model the hemodynamic consequences of TMVR on a patient-specific basis. CFD modeling is generally used to reveal complex flow patterns that cannot otherwise be visualized with conventional imaging techniques. The workflow for performing a patient-specific CFD simulation involves (1) image acquisition, (2) image processing, (3) simulation set-up, and (4) analysis of the simulation results. Steps 1 and 2 of this workflow are further illustrated in Figure 2. After completing the CFD simulation, the resultant flow patterns can be visualized using a virtual reality platform (shown here is the EchoPixel True3D Viewer). Evaluating the accuracy of the simulated flow field is of critical importance prior to using simulation results for clinical decision making. The accuracy of the CFD simulation relies on accurately modeling the patient anatomy and specifying realistic boundary conditions. These boundary conditions specify either velocity or pressure along the boundary of the flow domain. For a patient-specific simulation, the patient’s hemodynamic status may be used to determine a patient-specific pressure condition, while imaging modalities (e.g., CT, echocardiography, or phase-contrast magnetic resonance imaging (PCMRI)) may be used to determine patient-specific velocity conditions. Common velocity boundary conditions include mass flow rate, LV wall motion, and spatially-resolved velocity profiles. Ultimately, careful validation of the CFD model with in vitro or clinical data is necessary to ensure the simulated flow fields are accurate.
Analysis of the time-velocity curve in phase-contrast magnetic resonance imaging: a phantom study
Published in Computer Assisted Surgery, 2019
Jieun Park, Junghun Kim, Yongmin Chang, Sung Won Youn, Hui Joong Lee, Eun-Ju Kang, Ki-Nam Lee, Vojtěch Suchánek, Sinjae Hyun, Jongmin Lee
Investigating flow information in vessels is necessary to diagnosis and treatment of cardiovascular disease [1–3]. The most popular non-invasive methods for measuring flow related parameters are Doppler ultrasonography (US) and phase-contrast magnetic resonance imaging (PC-MRI). Doppler US with better temporal resolution measures the blood flow in real-time [4,5]. However, Doppler US highly dependent on operator because it requires considerable technical ability like entry angle correction [6]. PC-MRI can be used for measuring blood flow velocity non-invasively by using phase shifts in moving spins to quantify the velocity information in the flow of vessels [7–10]. The PC-MRI is suitable in the various field because it is user-independent and offers the possibility of analyzing vascular hemodynamics without restrictions to anatomic coverage [11].