Bergersen, Aslak

Mancini, Viviana

Segers, Patrick

Valen-Sendstad, Kristian

Vierendeels, Jan

Ghent University Academic Bibliography

1/4/2018 Oxford Abstractshttps://app.oxfordabstracts.com/events/123/submissions/21389/abstract-book-view 1/2Can We Detect Carotid Artery Stenosis From Skin Vibrations: AComputational Investigation of High-Frequent Flow Under PhysiologicalVarying Flow Conditions.Viviana Mancini1, Aslak Bergersen2, Jan Vierendeels3, Patrick Segers1, Kristian Valen-Sendstad21Ghent University, bioMMeda, Ghent, Belgium. 2Simula Research Laboratory,Computational Cardiac Modelling, Lysaker, Norway. 3Ghent University, Department ofFlow, heat and combustion mechanics, Ghent, Belgium.AbstractIntroduction Carotid artery stenoses are a major cause of stroke with a prevalence of 4.2% in thepopulation1 and are often asymptomatic. Therefore, it is a need for a quick andportable screening method. A newly suggested method for screening is using a laserDoppler vibrometer, which can detect skin vibrations induced by a high-frequent post-stenotic flow. However, we do not know if all stenoses harbor such instabilities. The aimof this study was to investigate how high-frequent flow instabilities vary with the degreeof stenosis, inlet flow rate, and outlet flow split. Methods A computed tomography image of a 76 % stenosed internal carotid artery (ICA) wassegmented and then altered to obtain models ranging from a mild stenosis to almostcomplete occlusion: 56%, 66%, 86%, and 96%, as illustrated in Fig1A. The inlet flowrates, with a physiologically plausible waveform2, and outflow splits were varied withmean plus/minus one and two standard deviations3,4, respectively, see Fig1C-D.Furthermore, since flow rates and splits vary with the degree of stenosis, we alsochanged these alongside with the degree of stenosis, see Fig1B. We assumed blood tobehave as a Newtonian fluid with kinematic viscosity ν=3.3·10-6m2/s and used ourkinetic energy preserving and minimally dissipative, second order solver Oasis5 tosimulate for two cardiac cycles and reported the velocity magnitude from the lattermeasured in point P, see Fig1A. We used 20.000 time steps per cycle and meshes with~15 million linear elements, resulting in an average cell length of Δxmean=1.92·10-4m.Results Fig1E shows the qualitative results from varying the degree of stenosis, and we canobserve that the models with a stenosis severity less than 70 % harbored high-frequentfluctuations. Fig1F-G shows the results from varying the flow rate and split,respectively, and both exhibited similar trends; increased ICA flow rate enlarged thefluctuations.1/4/2018 Oxford Abstractshttps://app.oxfordabstracts.com/events/123/submissions/21389/abstract-book-view 2/2Discussion We only observed fluctuations in moderate (< 70 %) stenosis models, which canphysiologically be explained by a reduction in flow rate due to an increased pressuredrop over the stenosis. If the simulated results reflect physiology, and not modelingassumptions, the LDV is promising for detecting early-stage carotid artery stenosis. Infuture work, we will investigate flow fluctuations in healthy subjects to understand therisk of false positives.Acknowledgments Funded by H2020 ICT-26-2014 CARDIS.References 1. de Weerd M et al., Stroke 40,1105–1113 (2009). 2. Hoi Y et al., Physiol. Meas. 31,291-302 (2010). 3. Groen HC et al., J. Biomech. 43, 2332–2338 (2010). 4. Likittanasombut P et al., J. Neuroimaging 16,34–38 (2006). 5. Mortensen M & Valen-Sendstad K, Comput. Phys. Commun. (2016).

Can we detect carotid artery stenosis from skin vibrations : a computational investigation of high-frequent flow under physiological varying flow conditions

https://biblio.ugent.be/publication/8575339/file/8575342.pdf

Can we detect carotid artery stenosis from skin vibrations : a computational investigation of high-frequent flow under physiological varying flow conditions

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