Purpose
Screening of asymptomatic carotid stenoses is performed by auscultation of the carotid bruit, but the sensitivity is poor. Instead, it has been suggested to detect carotid bruit as neck’s skin vibrations. We here take a first step towards a computational fluid dynamics proof-of-concept study, and investigate the robustness of our numerical approach for capturing high-frequent fluctuations in the post-stenotic flow. The aim was to find an ideal solution strategy from a pragmatic point of view, balancing accuracy with computational cost comparing an under-resolved direct numerical simulation (DNS) approach vs. three common large eddy simulation (LES) models (static/dynamic Smagorinsky and Sigma).
Method
We found a reference solution by performing a spatial and temporal refinement study of a stenosed carotid bifurcation with constant flow rate. The reference solution
was compared against LES for both a constant and pulsatile flow.
Results
Only the Sigma and Dynamic Smagorinsky models were able to replicate the flow field of the reference solution for a pulsatile simulation, however the computational cost of the Sigma model was lower. However, none of the sub-grid scale models were able to replicate the high-frequent flow in the peak-systolic constant flow rate simulations, which had a higher mean Reynolds number.
Conclusions
The Sigma model was the best combination between accuracy and cost for simulating the pulsatile post-stenotic flow field, whereas for the constant flow rate, the under-resolved DNS approach was better. These results can be used as a reference for future studies investigating high-frequent flow features.
Early detection of asymptomatic carotid stenosis may help identifying individuals at risk of stroke. We explore a new method based on laser Doppler vibrometry (LDV) which could allow the non-contact detection of stenosis from neck skin vibrations due to stenosis-induced flow disturbances. Experimental fluid dynamical tests were performed with water on a severely stenosed patient-specific carotid bifurcation model. Measurements were taken under various physiological flow regimes both in a compliant and stiff-walled version of the model, at 1 to 4 diameters downstream from the stenosis. An inter-arterial pressure catheter was positioned as reference. Increasing flow led to corresponding increase in power spectral density (PSD) of pressure and LDV recordings in the 0–500 Hz range. The stiff model lead to higher PSD. PSD of the LDV signal was less dependent on the downstream measurement location than pressure. The strength of the association between PSD and flow level, model material and measuring location was highest in the 0–50 Hz range, however useful information was found up to 200 Hz. This proof-of-concept suggests that LDV has the potential to detect stenosis-induced disturbed flow. Further computational and clinical validation studies are ongoing to assess the sensitivity and specificity of the technique for clinical screening.
Early detection of asymptomatic carotid stenosis is crucial for treatment planning in the prevention of ischemic stroke. Auscultation, the current first-line screening methodology, comes with severe limitations that create urge for novel and robust techniques. Laser Doppler vibrometer (LDV) is a promising tool for inferring carotid stenosis by measuring stenosis-induced vibrations. The goal of the current study was to evaluate the feasibility of LDV for carotid stenosis detection. LDV measurements on a carotid phantom were used to validate our previously verified high-resolution computational fluid dynamics methodology, which was used to evaluate the impact of flowrate, flow split, and stenosis severity on the poststenotic intensity of flow instabilities (IFI). We evaluated sensitivity, specificity, and accuracy of using IFI for stenoses detection. Linear regression analyses showed that computationally derived pressure fluctuations correlated (R2 = 0.98) with LDV measurements of stenosis-induced vibrations. The flowrate of stenosed vessels correlated (R2 = 0.90) with the presence of poststenotic instabilities. Receiver operating characteristic analyses of power spectra revealed that the most relevant frequency bands for the detection of moderate (56–76%) and severe (86–96%) stenoses were 80–200 Hz and 0–40 Hz, respectively. Moderate stenosis was identified with sensitivity and specificity of 90%; values decreased to 70% for severe stenosis. The use of LDV as screening tool for asymptomatic stenosis can potentially provide improved accuracy of current screening methodologies for early detection. The applicability of this promising device for mass screening is currently being evaluated clinically.
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