The influence of an unilateral carotid artery stenosis on brain oxygenation

Köppl, Tobias; Schneider, M.; Pohl, Ulrich; Wohlmuth, Barbara

doi:10.1016/j.medengphy.2014.03.020

Cited by 12 publications

(23 citation statements)

References 27 publications

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“…Peak flow rates of %2:8 ml=s and %1:0 ml=s were observed at the AcoA and RPcoA, respectively, when the RCCA was occluded. These flow rates are consistent with a recent study by K€ oppl et al [13] which reported %3:2 ml=s and %1:5 ml=s for peak systolic flow rates at the AcoA and RPcoA, respectively, for severe RCCA stenosis (95% area reduction). We note that their study used 2 Â larger vessel areas for the communicating arteries than the present study, which may explain the observed differences, in addition to the more sophisticated CA model in our study.…”

Section: Discussionsupporting

confidence: 92%

“…In a subsequent publication [12], the threeelement Windkessel models were extended to include autoregulatory function to investigate how anatomical variation of the CoW restored CBF after a sudden carotid occlusion. Using similar models, K€ oppl et al [13] also recently studied the impact of varying degrees of unilateral stenoses of a carotid artery to CBF. The three-element based CA models used in the aforementioned studies demonstrated the ability of CA to recover and maintain blood flow following carotid artery occlusion.…”

Section: Introductionmentioning

confidence: 99%

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A Coupled Lumped-Parameter and Distributed Network Model for Cerebral Pulse-Wave Hemodynamics

Ryu

Shadden

2015

Journal of Biomechanical Engineering

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The cerebral circulation is unique in its ability to maintain blood flow to the brain under widely varying physiologic conditions. Incorporating this autoregulatory response is necessary for cerebral blood flow (CBF) modeling, as well as investigations into pathological conditions. We discuss a one-dimensional (1D) nonlinear model of blood flow in the cerebral arteries coupled to autoregulatory lumped-parameter (LP) networks. The LP networks incorporate intracranial pressure (ICP), cerebrospinal fluid (CSF), and cortical collateral blood flow models. The overall model is used to evaluate changes in CBF due to occlusions in the middle cerebral artery (MCA) and common carotid artery (CCA). Velocity waveforms at the CCA and internal carotid artery (ICA) were examined prior and post MCA occlusion. Evident waveform changes due to the occlusion were observed, providing insight into cerebral vasospasm monitoring by morphological changes of the velocity or pressure waveforms. The role of modeling of collateral blood flows through cortical pathways and communicating arteries was also studied. When the MCA was occluded, the cortical collateral flow had an important compensatory role, whereas the communicating arteries in the circle of Willis (CoW) became more important when the CCA was occluded. To validate the model, simulations were conducted to reproduce a clinical test to assess dynamic autoregulatory function, and results demonstrated agreement with published measurements.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

A Coupled Lumped-Parameter and Distributed Network Model for Cerebral Pulse-Wave Hemodynamics

Ryu

Shadden

2015

Journal of Biomechanical Engineering

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“…21,2, chap. 10 To describe the dynamics of a Windkessel model, the following ODE has been derived using averaging techniques and an analogy from electrical science 4,21,25 :…”

Section: Modelling Of the Peripheral Circulationmentioning

confidence: 99%

“…To solve the ODEs occurring in Section 2, we use an explicit discretisation scheme of first order. 40 1.15 · 10 0 6.00 · 10 −4 1.50 · 10 0 7.75 · 10 −4 1.51 · 10 0 7.85 · 10 −4 160 9.53 · 10 −1 4.97 · 10 −4 1.23 · 10 0 6.35 · 10 −4 1.25 · 10 0 6.47 · 10 −4 640 2.95 · 10 −1 1.54 · 10 −4 2.98 · 10 −1 1.54 · 10 −4 3.01 · 10 −1 1.56 · 10 −4 Flow rate 10 6.05 · 10 0 3.35 · 10 −2 5.48 · 10 0 2.51 · 10 −1 8.33 · 10 −1 1.20 · 10 −2 40 7.72 · 10 −1 4.28 · 10 −3 5.23 · 10 −1 2.41 · 10 −2 1.84 · 10 −1 2.64 · 10 −3 160 6.35 · 10 −1 3.52 · 10 −3 4.08 · 10 −1 1.88 · 10 −2 1.41 · 10 −1 2.03 · 10 −3 640 1.73 · 10 −1 9.61 · 10 −4 7.70 · 10 −2 3.55 · 10 −3 3.02 · 10 −2 4.34 · 10 −4 a The table shows the absolute (E A ) and relative (E R ) errors obtained with datasets of increasing size N.…”

Section: Simulation Parametersunclassified

“…2018;34:e3095.wileyonlinelibrary.com/journal/cnm understand how the impact of an occlusion on blood flow can be reduced. [8][9][10] Further important application areas are the stability analysis of an implanted stent or an aneurysm. Thereby, it is crucial to compute realistic pressure values, mass fluxes, and wall shear stresses in an efficient way such that these data can be evaluated as fast as possible.…”

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confidence: 99%

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Numerical modelling of a peripheral arterial stenosis using dimensionally reduced models and kernel methods

Köppl

Santin

Haasdonk

et al. 2018

Numer Methods Biomed Eng

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In this work, we consider 2 kinds of model reduction techniques to simulate blood flow through the largest systemic arteries, where a stenosis is located in a peripheral artery, i.e., in an artery that is located far away from the heart. For our simulations, we place the stenosis in one of the tibial arteries belonging to the right lower leg (right posterior tibial artery). The model reduction techniques that are used are on the one hand dimensionally reduced models (1-D and 0-D models, the so-called mixed-dimension model) and on the other hand surrogate models produced by kernel methods. Both methods are combined in such a way that the mixed-dimension models yield training data for the surrogate model, where the surrogate model is parametrised by the degree of narrowing of the peripheral stenosis. By means of a well-trained surrogate model, we show that simulation data can be reproduced with a satisfactory accuracy and that parameter optimisation or state estimation problems can be solved in a very efficient way. Furthermore, it is demonstrated that a surrogate model enables us to present after a very short simulation time the impact of a varying degree of stenosis on blood flow, obtaining a speedup of several orders over the full model.

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A 3D‐1D coupled blood flow and oxygen transport model to generate microvascular networks

Köppl¹,

Vidotto²,

Wohlmuth

2020

Numer Methods Biomed Eng

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In this work, we introduce an algorithmic approach to generate microvascular networks starting from larger vessels that can be reconstructed without noticeable segmentation errors. Contrary to larger vessels, the reconstruction of fine-scale components of microvascular networks shows significant segmentation errors, and an accurate mapping is time and cost intense. Thus there is a need for fast and reliable reconstruction algorithms yielding surrogate networks having similar stochastic properties as the original ones. The microvascular networks are constructed in a marching way by adding vessels to the outlets of the vascular tree from the previous step. To optimise the structure of the vascular trees, we use Murray's law to determine the radii of the vessels and bifurcation angles. In each step, we compute the local gradient of the partial pressure of oxygen and adapt the orientation of the new vessels to this gradient. At the same time, we use the partial pressure of oxygen to check whether the considered tissue block is supplied sufficiently with oxygen. Computing the partial pressure of oxygen, we use a 3D-1D coupled model for blood flow and oxygen transport. To decrease the complexity of a fully coupled 3D model, we reduce the blood vessel network to a 1D graph structure and use a bi-directional coupling with the tissue which is described by a 3D homogeneous porous medium. The resulting surrogate networks are analysed with respect to morphological and physiological aspects. K E Y W O R D S 3D-1D coupled flow models, blood flow simulations, dimensionally reduced models, flows in porous media, oxygen transport, vascular growth

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The influence of an unilateral carotid artery stenosis on brain oxygenation

Cited by 12 publications

References 27 publications

A Coupled Lumped-Parameter and Distributed Network Model for Cerebral Pulse-Wave Hemodynamics

A Coupled Lumped-Parameter and Distributed Network Model for Cerebral Pulse-Wave Hemodynamics

Numerical modelling of a peripheral arterial stenosis using dimensionally reduced models and kernel methods

A 3D‐1D coupled blood flow and oxygen transport model to generate microvascular networks

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