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Published on: May 8, 2012
Development and Evaluation of a Multifrequency Ultrafast Doppler Spectral Analysis (MFUDSA) Algorithm for Wall Shear
Andrew J Malone1,2, Seán Cournane3, Izabela Naydenova1
1School of Physics, Clinical and Optometric Sciences, IEO Centre, Faculty of Science and Health, Technological University Dublin, D07 H6K8 Dublin, Ireland.
Insights
A new Multifrequency ultrafast Doppler spectral analysis (MFUDSA) algorithm improves wall shear stress (WSS) measurement in atherosclerotic plaque. This offers potential for earlier cardiovascular disease diagnosis compared to existing methods.
Area of Science:
- Biomedical Engineering
- Cardiovascular Imaging
- Medical Diagnostics
Background:
- Cardiovascular diseases are a leading cause of death globally.
- Current diagnostic methods primarily focus on vessel anatomy, potentially missing early disease indicators.
- Wall shear stress (WSS) is an emerging biomarker for early atherosclerotic disease detection.
Purpose of the Study:
- To introduce and validate a novel algorithm, Multifrequency ultrafast Doppler spectral analysis (MFUDSA), for quantifying WSS in atherosclerotic plaque.
- To compare the performance of MFUDSA against existing WSS assessment techniques.
- To evaluate the potential of MFUDSA for earlier cardiovascular disease diagnosis.
Main Methods:
- Development and optimization of the MFUDSA algorithm using simulations and in-vitro flow phantom experiments.
- Comparison of MFUDSA with standard pulsed-wave Doppler, Ultrafast Doppler, Parabolic Doppler, and plane-wave Doppler.
- Assessment of signal-to-noise ratio (SNR) and velocity resolution improvements.
Main Results:
- MFUDSA demonstrated a 4-8 fold increase in SNR and a 1.10-1.35 fold increase in velocity resolution compared to 1D Fourier analysis.
- MFUDSA significantly differentiated WSS values between moderate (p = 0.003) and severe (p = 0.001) disease progression.
- The algorithm showed superior performance in WSS assessment.
Conclusions:
- MFUDSA is a promising novel algorithm for accurate WSS quantification in atherosclerotic plaque.
- The enhanced performance of MFUDSA suggests its potential for earlier and more precise cardiovascular disease diagnosis.
- This technique may offer advantages over current anatomical imaging methods for risk stratification.
Abstract:
Cardiovascular pathology is the leading cause of death and disability in the Western world, and current diagnostic testing usually evaluates the anatomy of the vessel to determine if the vessel contains blockages and plaques. However, there is a growing school of thought that other measures, such as wall shear stress, provide more useful information for earlier diagnosis and prediction of atherosclerotic related disease compared to pulsed-wave Doppler ultrasound, magnetic resonance angiography, or computed tomography angiography. A novel algorithm for quantifying wall shear stress (WSS) in atherosclerotic plaque using diagnostic ultrasound imaging, called Multifrequency ultrafast Doppler spectral analysis (MFUDSA), is presented. The development of this algorithm is presented, in addition to its optimisation using simulation studies and in-vitro experiments with flow phantoms approximating the early stages of cardiovascular disease. The presented algorithm is compared with commonly used WSS assessment methods, such as standard PW Doppler, Ultrafast Doppler, and Parabolic Doppler, as well as plane-wave Doppler. Compared to an equivalent processing architecture with one-dimensional Fourier analysis, the MFUDSA algorithm provided an increase in signal-to-noise ratio (SNR) by a factor of 4-8 and an increase in velocity resolution by a factor of 1.10-1.35. The results indicated that MFUDSA outperformed the others, with significant differences detected between the typical WSS values of moderate disease progression (p = 0.003) and severe disease progression (p = 0.001). The algorithm demonstrated an improved performance for the assessment of WSS and has potential to provide an earlier diagnosis of cardiovascular disease than current techniques allow.
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