ArticleIEEE transactions on ultrasonics, ferroelectrics, and frequency control2024
Quantitative Viscoelastic Response (QVisR): Direct Estimation of Viscoelasticity With Neural Networks.
Article in IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- VESTA: Machine Learning-Enabled Estimation of ViscoElastic Ratios from On-Axis Spatio-Temporal ARFI Features.bioRxiv : the preprint server for biology · 2026Article
- Deep-DoPIo: Deep Learning for Shear Elasticity Prediction in Double-Profile Intersection Ultrasound.IEEE transactions on ultrasonics · 2026Article
- VisR Ultrasound With Non-Normal ARF-AoS Incidence Interrogates Both Shear and Young's Elastic Moduli in Transversely Isotropic Materials.IEEE open journal of ultrasonics, ferroelectrics, and frequency control · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
We present a machine learning method to directly estimate viscoelastic moduli from displacement time-series profiles generated by viscoelastic response (VisR) ultrasound excitations. VisR uses two colocalized acoustic radiation force (ARF) pushes to approximate tissue viscoelastic creep response and tracks displacements on-axis to measure the material relaxation. A fully connected neural network is trained to learn a nonlinear mapping from VisR displacements, the push focal depth, and the measurement axial depth to the material elastic and viscous moduli. In this work, we assess the validity of quantitative VisR (QVisR) in simulated materials, propose a method of domain adaption to phantom VisR displacements, and show in vivo estimates from a clinically acquired dataset.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.