ArticleComputers in biology and medicine2021
Viscoelastic parameter estimation using simulated shear wave motion and convolutional neural networks.
Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Rheological analysis in food processing: factors, applications, and future outlooks with machine learning integration.RSC advances · 2026Review
- Estimating shear modulus of isotropic materials from scanning laser Doppler vibrometry via convolutional neural networks.Journal of the mechanical behavior of biomedical materials · 2025Article
- Shear wave elastography primer for the abdominal radiologist.Abdominal radiology (New York) · 2025Review
- Twin Peak Method for Estimating Tissue Viscoelasticity Using Shear Wave Elastography.Ultrasound in medicine & biology · 2025Article
- A Comprehensive Review on the Viscoelastic Parameters Used for Engineering Materials, Including Soft Materials, and the Relationships between Different Damping Parameters.Sensors (Basel, Switzerland) · 2024Review
- Kidney cortex shear wave motion simulations based on segmented biopsy histology.Computer methods and programs in biomedicine · 2024Article
- Improved two-point frequency shift power method for measurement of shear wave attenuation.Ultrasonics · 2022Article
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Authors and funding
3 authors.
Funding
Abstract
Ultrasound shear wave elastography (SWE) techniques have been very useful for the analysis of tissue rheological properties, but there are still obstacles for robust evaluation of viscoelastic tissue properties. In this proof-of-concept study, we investigate whether convolutional neural networks (CNN) are capable of retrieving the elasticity and viscosity parameters from simulated shear wave motion images. Staggered-grid finite difference simulations based on a Kelvin-Voigt rheological model were used to generate data for this study. The wave motion datasets were created using Kelvin-Voigt shear elasticity values ranging from 1 to 25 kPa, shear viscosities ranging from 0 to 10 Pa⋅s, and two different push profiles using f-numbers of 1 and 2. The CNN architectures, optimized using mean squared error loss, were then trained to retrieve a specific viscoelastic parameter. Both elasticity and viscosity values were successfully retrieved, with regression R
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