Enhanced shear wave attenuation estimation with expanded bandwidth in viscoelastic media

Ultrasound shear wave elastography (SWE) is widely employed to differentiate healthy from pathological tissues based on their viscoelastic mechanical properties. Although elasticity has traditionally been the main focus, tissue viscosity also plays a crucial role in characterizing mechanical behavior using SWE. Numerous methods have been developed for estimating the viscosity, with rheological model-based approaches being the most widely used. However, model-free techniques are gaining increasing attention, as they do not impose a predetermined relationship between wave velocity and attenuation, offering greater flexibility in capturing complex tissue viscoelastic behavior. In this study, we propose a novel method for calculating the shear wave attenuation. The approach, termed SAGA-ST, integrates a Super-Gaussian window-based Stockwell transform with slant frequency–wavenumber (f-k) analysis and leverages the full width at half-maximum of the f-k spectrum. The SAGA-ST method was first evaluated using analytical phantom data in tissue-mimicking viscoelastic media. To further assess its robustness, we applied the method to analytical shear wave motion data corrupted with varying levels of additive white Gaussian noise. Experimental validation was also performed using data from custom-designed tissue-mimicking phantoms and ex vivo bovine liver sample. We compared the performance of SAGA-ST with two existing techniques: the two-dimensional Fourier transform (2D-FT) and the generalized Stockwell transform-based method (GST-SFK). The proposed SAGA-ST method consistently demonstrates superior performance in analytical phantoms, exhibiting lower median attenuation bias and a smaller interquartile range. This is further supported by data points predominantly falling within the acceptable bias region and a median attenuation bias of less than 1% across all signal-to-noise ratio levels and frequency ranges, collectively indicating enhanced accuracy and precision. Furthermore, SAGA-ST extends the usable bandwidth for attenuation estimation, compared to the 2D-FT-based method, offering improved accuracy for tissue characterization.超声剪切波弹性成像（Ultrasound shear wave elastography, SWE）已被广泛用于基于组织的粘弹性力学特性区分健康组织与病变组织。尽管传统上弹性一直是研究的核心关注点，但组织粘度在利用剪切波弹性成像表征力学行为的过程中同样发挥着关键作用。目前已开发出多种粘度估算方法，其中基于流变学模型的手段应用最为广泛。然而，无模型技术正受到越来越多的关注，因其无需预设波速与衰减之间的固定关系，能够更灵活地捕捉复杂的组织粘弹性行为。本研究提出了一种全新的剪切波衰减计算方法。该方法被命名为SAGA-ST，将基于超高斯窗的斯托克韦尔变换（Stockwell transform）与倾斜频率-波数（slant frequency–wavenumber, f-k）分析相结合，并利用了f-k频谱的半高全宽参数。研究首先使用组织模拟粘弹性介质中的分析体模数据，对SAGA-ST方法进行了评估。为进一步评估该方法的鲁棒性，我们将其应用于受不同强度加性高斯白噪声干扰的分析类剪切波运动数据。同时，我们还使用定制组织模拟体模以及离体牛肝样本的数据完成了实验验证。我们将SAGA-ST的性能与两种现有技术进行了对比：二维傅里叶变换（two-dimensional Fourier transform, 2D-FT）以及基于广义斯托克韦尔变换的方法（generalized Stockwell transform-based method, GST-SFK）。所提出的SAGA-ST方法在分析体模实验中始终表现出更优的性能，其衰减偏差的中位数更低，四分位距也更小。绝大多数数据点均落在可接受的偏差范围内，且在所有信噪比水平与频率区间内，衰减偏差的中位数均低于1%，这进一步佐证了该方法的优势，共同表明其具备更高的准确性与精度。此外，相较于基于二维傅里叶变换的方法，SAGA-ST拓展了衰减估算的可用带宽，能够为组织表征提供更精准的结果。