4.6 Article

Development and Evaluation of a Multifrequency Ultrafast Doppler Spectral Analysis (MFUDSA) Algorithm for Wall Shear Stress Measurement: A Simulation and In Vitro Study

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DIAGNOSTICS
卷 13, 期 11, 页码 -

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MDPI
DOI: 10.3390/diagnostics13111872

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flow phantom; biomedical signal processing; cardiovascular disease; Doppler ultrasound; quantitative ultrasound; signal processing algorithms; ultrasonography; wall shear stress

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Cardiovascular pathology is a major cause of death and disability in the Western world. Traditional diagnostic testing evaluates the anatomy of the vessel for blockages and plaques, but there is a belief that measuring wall shear stress provides more useful information for early diagnosis and prediction of atherosclerotic disease. A new algorithm called Multifrequency ultrafast Doppler spectral analysis (MFUDSA) was developed to quantify wall shear stress in atherosclerotic plaque using diagnostic ultrasound imaging. The algorithm outperformed other commonly used assessment methods and has the potential to provide earlier diagnosis of cardiovascular disease than current techniques.
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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