scholarly journals 4.4 CAN LASER DOPPLER VIBROMETER DETECT CAROTID STENOSIS FROM SKIN VIBRATIONS? HYDRAULIC BENCH TESTS ON PATIENT-SPECIFIC MODEL

2018 ◽  
Vol 24 (C) ◽  
pp. 76
Author(s):  
Viviana Mancini ◽  
Daniela Tommasin ◽  
Yanlu Li ◽  
Roel Baets ◽  
Stephen Greenwald ◽  
...  
Keyword(s):  
Carotid Stenosis ◽  
Laser Doppler Vibrometer ◽  
Specific Model ◽  
Laser Doppler ◽  
Patient Specific ◽  
Bench Tests

scholarly journals Computed Poststenotic Flow Instabilities Correlate Phenotypically With Vibrations Measured Using Laser Doppler Vibrometry: Perspectives for a Promising In Vivo Device for Early Detection of Moderate and Severe Carotid Stenosis

2020 ◽  
Vol 142 (9) ◽  
Author(s):  
Viviana Mancini ◽  
Aslak W. Bergersen ◽  
Kristian Valen-Sendstad ◽  
Patrick Segers
Keyword(s):  
Early Detection ◽  
Carotid Stenosis ◽  
Pressure Fluctuations ◽  
Laser Doppler Vibrometer ◽  
Power Spectra ◽  
Laser Doppler ◽  
Flow Instabilities ◽  
Promising Tool ◽  
The Impact

Abstract 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.

scholarly journals Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Gaoyang Li ◽  
Haoran Wang ◽  
Mingzi Zhang ◽  
Simon Tupin ◽  
Aike Qiao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Artery Bypass ◽  
Computational Cost ◽  
Specific Model ◽  
Arterial System ◽  
Patient Specific ◽  
Cardiovascular Hemodynamics ◽  
Before And After ◽  
Deep Learning Network ◽  
Cfd Method

AbstractThe clinical treatment planning of coronary heart disease requires hemodynamic parameters to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in the simulation of cardiovascular hemodynamics. However, for the patient-specific model, the complex operation and high computational cost of CFD hinder its clinical application. To deal with these problems, we develop cardiovascular hemodynamic point datasets and a dual sampling channel deep learning network, which can analyze and reproduce the relationship between the cardiovascular geometry and internal hemodynamics. The statistical analysis shows that the hemodynamic prediction results of deep learning are in agreement with the conventional CFD method, but the calculation time is reduced 600-fold. In terms of over 2 million nodes, prediction accuracy of around 90%, computational efficiency to predict cardiovascular hemodynamics within 1 second, and universality for evaluating complex arterial system, our deep learning method can meet the needs of most situations.

Temperature coefficient of Young’s modulus of silver microwhiskers determined by a laser Doppler vibration measurement

2021 ◽  
pp. 2150350
Author(s):  
Yijun Jiang ◽  
Mingyuan Lu ◽  
Shiliang Wang ◽  
Han Huang
Keyword(s):  
Temperature Dependence ◽  
Temperature Coefficient ◽  
Single Crystals ◽  
Young’S Modulus ◽  
Vapor Deposition ◽  
Physical Vapor Deposition ◽  
Young's Modulus ◽  
Laser Doppler Vibrometer ◽  
Laser Doppler ◽  
Vibration Measurement

Temperature dependence of Young’s modulus of Ag microwhiskers was determined by a laser Doppler vibrometer. The Ag whiskers with diameters in sub-microns were synthesized by the use of physical vapor deposition (PVD). They have a five-fold twinned structure grown along the [1 1 0] direction. The temperature coefficient of Young’s modulus was measured to be [Formula: see text] ppm/K in the range of 300 K to 650 K. The measured values are very close to the reported values of [Formula: see text] ppm/K for bulk Ag single crystals. This finding can benefit the design of Ag-based micro/nano-electromechanical systems or micro/nano-interconnectors operated at elevated or lowered temperatures.

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