Contrast Agent Bolus Dispersion in a Realistic Coronary Artery Geometry: Influence of Outlet Boundary Conditions

Sommer, Karsten; Schmidt, Regine; Graafen, Dirk; Breit, Hanns‐Christian; Schreiber, Laura M.

doi:10.1007/s10439-013-0950-z

Cited by 12 publications

(23 citation statements)

References 46 publications

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“…However, looking at the results for the stress simulations, the relative increase between the last 2 distance segments is comparably higher. This results in what can be interpreted as a starting asymptotic behavior of ΔMPR toward larger traveled distances, in accordance with Sommer et al Comparing the behavior of the rest and hyperemic perfusion quantification errors with the results obtained for RD AIF from Figure F,H, this is expected. The flatter slope of RD AIF at stress with respect to the integrated distance indicates that under stress the relative contribution of distal vessels to CA dispersion is increased in comparison to the rest condition.…”

Section: Resultssupporting

confidence: 91%

“…The vascular geometries are discretized using the software package cfMesh (Creative Fields, London, United Kingdom), yielding mesh sizes of 13 830 835 (96% hexahedral type, duration mesh creation: 1.5 hours) on the LCT grid and 6 975 193 (95%, 20 minutes) on the RCT grid. The chosen resolution is based on the mesh convergence analyses in Refs .…”

Section: Methodsmentioning

confidence: 99%

“…The parameters to generate tissue concentration time curves Conc Myo from Figure with MMID4 can be set within the software JSim and are chosen as in a number of other studies . At rest, an MBF of 1 mL/min/g is assumed.…”

Section: Methodsmentioning

confidence: 99%

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Influence of contrast agent dispersion on bolus‐based MRI myocardial perfusion measurements: A computational fluid dynamics study

Martens

Panzer

Wijngaard

et al. 2019

Magnetic Resonance in Med

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Purpose Bolus‐based dynamic contrast agent (CA) perfusion measurements of the heart are subject to systematic errors due to CA bolus dispersion in the coronary arteries. To better understand these effects on quantification of myocardial blood flow and myocardial perfusion reserve (MPR), an in‐silico model of the coronary arteries down to the pre‐arteriolar vessels has been developed. Methods In this work, a computational fluid dynamics analysis is performed to investigate these errors on the basis of realistic 3D models of the left and right porcine coronary artery trees, including vessels at the pre‐arteriolar level. Using advanced boundary conditions, simulations of blood flow and CA transport are conducted at rest and under stress. These are evaluated with regard to dispersion (assessed by the width of CA concentration time curves and associated vascular transport functions) and errors of myocardial blood flow and myocardial perfusion reserve quantification. Results Contrast agent dispersion increases with traveled distance as well as vessel diameter, and decreases with higher flow velocities. Overall, the average myocardial blood flow errors are −28% ± 16% and −8.5% ± 3.3% at rest and stress, respectively, and the average myocardial perfusion reserve error is 26% ± 22%. The calculated values are different in the left and right coronary tree. Conclusion Contrast agent dispersion is dependent on a complex interplay of several different factors characterizing the cardiovascular bed, including vessel size and integrated vascular length. Quantification errors evoked by the observed CA dispersion show nonnegligible distortion in dynamic CA bolus‐based perfusion measurements. We expect future improvements of quantitative perfusion measurements to make the systematic errors described here more apparent.

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Section: Resultssupporting

confidence: 91%

Section: Methodsmentioning

confidence: 99%

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Influence of contrast agent dispersion on bolus‐based MRI myocardial perfusion measurements: A computational fluid dynamics study

Martens

Panzer

Wijngaard

et al. 2019

Magnetic Resonance in Med

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“…For local WSS assessment proper outlet boundary conditions are essential (Sommer et al, 2013;Trachet et al, 2011;van de Vosse and Stergiopulos, 2011). In a previous study a diameterbased scaling law was derived to set the outflow for human Fig.…”

Section: Wssmentioning

confidence: 99%

Functional and anatomical measures for outflow boundary conditions in atherosclerotic coronary bifurcations

Schrauwen¹,

Coenen

Kurata

et al. 2016

Journal of Biomechanics

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“…Combining current imaging technology with image segmentation and geometric meshing tools, patient-specific models of systemic arterial mechanics have been used to investigate the effects of individualized geometry in cases on aortic stenosis ((LaDisa et al 2011), (Coogan et al 2013)), coronary artherosclerosis ((Huo et al 2007), (Kim et al 2010), (Waters et al 2011), (Sommer et al 2014)), and congenital heart disease ((Migliavacca et al 2006), (Spilker et al 2007)).…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous mechanics of the mouse pulmonary arterial network

Lee

Carlson

Chesler

et al. 2016

Biomech Model Mechanobiol

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Individualized modeling and simulation of blood flow mechanics find applications in both animal research and patient care. Individual animal or patient models for blood vessel mechanics are based on combining measured vascular geometry with a fluid structure model coupling formulations describing dynamics of the fluid and mechanics of the wall. For example, one-dimensional fluid flow modeling requires a constitutive law relating vessel cross-sectional deformation to pressure in the lumen. To investigate means of identifying appropriate constitutive relationships, an automated segmentation algorithm was applied to micro-computerized tomography (CT) images from a mouse lung obtained at four different static pressures to identify the static pressure-radius relationship for 4 generations of vessels in the pulmonary arterial network. A shape-fitting function was parameterized for each vessel in the network to characterize the nonlinear and heterogeneous nature of vessel distensibility in the pulmonary arteries. These data on morphometric and mechanical properties were used to simulate pressure and flow velocity propagation in the network using one-dimensional representations of fluid and vessel wall mechanics. Moreover, wave intensity analysis was used to study effects of wall mechanics on generation and propagation of pressure wave reflections. Simulations were conducted to investigate the role of linear versus nonlinear formulations of wall elasticity and homogeneous versus heterogeneous treatments of vessel wall properties. Accounting for heterogeneity, by parameterizing the pressure/distention equation of state individually for each vessel segment, was found to have little effect on the predicted pressure profiles and wave propagation compared to a homogeneous parameterization based on average behavior. However, substantially different results were obtained using a linear elastic thin shell model than were obtained using a nonlinear model that has a more physiologically realistic pressure versus radius relationship.

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Contrast Agent Bolus Dispersion in a Realistic Coronary Artery Geometry: Influence of Outlet Boundary Conditions

Cited by 12 publications

References 46 publications

Influence of contrast agent dispersion on bolus‐based MRI myocardial perfusion measurements: A computational fluid dynamics study

Influence of contrast agent dispersion on bolus‐based MRI myocardial perfusion measurements: A computational fluid dynamics study

Functional and anatomical measures for outflow boundary conditions in atherosclerotic coronary bifurcations

Heterogeneous mechanics of the mouse pulmonary arterial network

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