Malone Andrew J, Cournane Seán, Naydenova Izabela, Meaney James F, Fagan Andrew J, Browne Jacinta E
School of Physics, Clinical and Optometric Sciences, IEO Centre, Faculty of Science and Health, Technological University Dublin, D07 H6K8 Dublin, Ireland.
Tissue Engineering Research Group (TERG), Department of Anatomy, Royal College of Surgeons in Ireland, D02 YN77 Dublin, Ireland.
Diagnostics (Basel). 2023 May 27;13(11):1872. doi: 10.3390/diagnostics13111872.
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 ( = 0.003) and severe disease progression ( = 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.
心血管病理学是西方世界死亡和残疾的主要原因,目前的诊断测试通常评估血管的解剖结构,以确定血管是否存在堵塞和斑块。然而,越来越多的人认为,与脉冲波多普勒超声、磁共振血管造影或计算机断层血管造影相比,其他测量方法,如壁面剪应力,能为动脉粥样硬化相关疾病的早期诊断和预测提供更有用的信息。本文提出了一种利用诊断超声成像量化动脉粥样硬化斑块壁面剪应力(WSS)的新算法,称为多频超快多普勒频谱分析(MFUDSA)。除了使用模拟研究和体外实验对该算法进行优化外,还介绍了该算法的开发过程,这些实验使用了近似心血管疾病早期阶段的流动模型。将所提出的算法与常用的WSS评估方法进行了比较,如标准PW多普勒、超快多普勒、抛物线多普勒以及平面波多普勒。与采用一维傅里叶分析的等效处理架构相比,MFUDSA算法的信噪比(SNR)提高了4-8倍,速度分辨率提高了1.10-1.35倍。结果表明,MFUDSA的性能优于其他算法,在中度疾病进展(=0.003)和重度疾病进展(=0.001)的典型WSS值之间检测到显著差异。该算法在WSS评估方面表现出了改进的性能,并且有可能比现有技术更早地诊断心血管疾病。