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 ±5% 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.

Authors

Institutions

Publication Details

Journal
Computers in Biology and Medicine
Published
2025-09-23
DOI
https://doi.org/10.1016/j.compbiomed.2025.111042
Citations
1
Primary Topic
Ultrasonics and Acoustic Wave Propagation
Type
article
Field-Weighted Citation Impact
0.86
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Enhanced shear wave attenuation estimation with expanded bandwidth in viscoelastic media

Piotr Kijanka, Ramin Almasi
1 citations
Computers in Biology and Medicine
Ultrasonics and Acoustic Wave Propagation
0.86
article

Enhanced shear wave attenuation estimation with expanded bandwidth in viscoelastic media

Piotr Kijanka, Ramin Almasi
article en
1 citations

Abstract

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 ±5% 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.

Computers in Biology and MedicineVol. 197(Pt B)
Jagiellonian University (PL), AGH University of Krakow (PL)
Openalex Percentile: Top 25%
Ultrasonics and Acoustic Wave Propagation
0.86
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.