ArticlePhysics in medicine and biology2020
Locally optimized correlation-guided Bayesian adaptive regularization for ultrasound strain imaging.
Article in Physics in medicine and biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 24 citations in OpenAlex.
- Lagrangian deformation tracking for strain imaging.Progress in biomedical engineering (Bristol, England) · 2026Review
- 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
- MixTURE: L1-Norm-Based Mixed Second-Order Continuity in Strain Tensor Ultrasound Elastography.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2024Article
- In Vivo Adaptive Bayesian Regularized Lagrangian Carotid Strain Imaging for Murine Carotid Arteries and Its Associations With Histological Findings.Ultrasound in medicine & biology · 2023Article
- Preliminary Feasibility of Stress Myocardial Elastography for the Detection of Coronary Artery Disease.Ultrasound in medicine & biology · 2023Article
- In Vivo Longitudinal Monitoring of Cardiac Remodeling in Murine Ischemia Models With Adaptive Bayesian Regularized Cardiac Strain Imaging: Validation Against Histology.Ultrasound in medicine & biology · 2023Article
- Imaging of Single Transducer-Harmonic Motion Imaging-Derived Displacements at Several Oscillation Frequencies Simultaneously.IEEE transactions on medical imaging · 2022Article
- Murine cardiac fibrosis localization using adaptive Bayesian cardiac strain imaging in vivo.Scientific reports · 2022Article
- Bayesian Regularized Strain Imaging for Assessment of Murine Cardiac Function In vivo.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2021Article
- Spatiotemporal Bayesian Regularization for Cardiac Strain Imaging: Simulation andIEEE open journal of ultrasonics, ferroelectrics, and frequency control · 2021Article
- Evaluation of illness severity of neonate infectious pneumonia and neurobehavioral development through ultrasonography under adaption algorithm.Pakistan journal of medical sciences · 2021Article
- Improving Ultrasound Lateral Strain Estimation Accuracy using Log Compression of Regularized Correlation Function.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2020Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Ultrasound strain imaging utilizes radio-frequency (RF) ultrasound echo signals to estimate the relative elasticity of tissue under deformation. Due to the diagnostic value inherent in tissue elasticity, ultrasound strain imaging has found widespread clinical and preclinical applications. Accurate displacement estimation using pre and post-deformation RF signals is a crucial first step to derive high quality strain tensor images. Incorporating regularization into the displacement estimation framework is a commonly employed strategy to improve estimation accuracy and precision. In this work, we propose an adaptive variation of the iterative Bayesian regularization scheme utilizing RF similarity metric signal-to-noise ratio previously proposed by our group. The regularization scheme is incorporated into a 2D multi-level block matching (BM) algorithm for motion estimation. Adaptive nature of our algorithm is attributed to the dynamic variation of iteration number based on the normalized cross-correlation (NCC) function quality and a similarity measure between pre-deformation and motion compensated post-deformation RF signals. The proposed method is validated for either quasi-static and cardiac elastography or strain imaging applications using uniform and inclusion phantoms and canine cardiac deformation simulation models. Performance of adaptive Bayesian regularization was compared to conventional NCC and Bayesian regularization with fixed number of iterations. Results from uniform phantom simulation study show significant improvement in lateral displacement and strain estimation accuracy. For instance, at 1.5% lateral strain in a uniform phantom, Bayesian regularization with five iterations incurred a lateral strain error of 104.49%, which was significantly reduced using our adaptive approach to 27.51% (p < 0.001). Contrast-to-noise (CNR
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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.