ArticlePhysics in medicine and biology2021
An unsupervised learning approach to ultrasound strain elastography with spatio-temporal consistency.
Article in Physics in medicine and biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 26 citations in OpenAlex.
- Self-supervised ultrasound B-mode strain elastography using SMURF.Ultrasonics · 2026Article
- Lagrangian deformation tracking for strain imaging.Progress in biomedical engineering (Bristol, England) · 2026Review
- A novel method for generating a fused ventilation-perfusion map using CT-derived ventilation and FDG-PET for lung functional avoidance in radiotherapy: A feasibility study.Precision radiation oncology · 2026Article
- AutoSOUL: Deep Convolutional Neural Network for Autonomous Selection of Ultrasound Speckle Tracking Parameters.IEEE transactions on ultrasonics · 2026Article
- Ultrasound Displacement Tracking Techniques for Post-Stroke Myofascial Shear Strain Quantification.IEEE transactions on bio-medical engineering · 2026Article
- A concept for fully automated segmentation of bone in ultrasound imaging.Scientific reports · 2025Article
- MixTURE: L1-Norm-Based Mixed Second-Order Continuity in Strain Tensor Ultrasound Elastography.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2024Article
- Unsupervised deep learning-based displacement estimation for vascular elasticity imaging applications.Physics in medicine and biology · 2023Article
- A Data-Efficient Deep Learning Strategy for Tissue Characterization via Quantitative Ultrasound: Zone Training.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2023Article
- Robust Scatterer Number Density Segmentation of Ultrasound Images.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2022Article
- Spatiotemporal Bayesian Regularization for Cardiac Strain Imaging: Simulation andIEEE open journal of ultrasonics, ferroelectrics, and frequency control · 2021Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Quasi-static ultrasound elastography (USE) is an imaging modality that measures deformation (i.e. strain) of tissue in response to an applied mechanical force. In USE, the strain modulus is traditionally obtained by deriving the displacement field estimated between a pair of radio-frequency data. In this work we propose a recurrent network architecture with convolutional long-short-term memory decoder blocks to improve displacement estimation and spatio-temporal continuity between time series ultrasound frames. The network is trained in an unsupervised way, by optimising a similarity metric between the reference and compressed image. Our training loss is also composed of a regularisation term that preserves displacement continuity by directly optimising the strain smoothness, and a temporal continuity term that enforces consistency between successive strain predictions. In addition, we propose an open-access
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