ArticleIEEE open journal of ultrasonics, ferroelectrics, and frequency control2021
Spatiotemporal Bayesian Regularization for Cardiac Strain Imaging: Simulation and
Article in IEEE open journal of ultrasonics, ferroelectrics, and frequency control, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Lagrangian deformation tracking for strain imaging.Progress in biomedical engineering (Bristol, England) · 2026Review
- In Vivo Adaptive Bayesian Regularized Lagrangian Carotid Strain Imaging for Murine Carotid Arteries and Its Associations With Histological Findings.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
- A pilot study of ion current estimation by ANN from action potential waveforms.Journal of biological physics · 2022Article
- Murine cardiac fibrosis localization using adaptive Bayesian cardiac strain imaging in vivo.Scientific reports · 2022Article
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5 authors.
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Abstract
Cardiac strain imaging (CSI) plays a critical role in the detection of myocardial motion abnormalities. Displacement estimation is an important processing step to ensure the accuracy and precision of derived strain tensors. In this paper, we propose and implement Spatiotemporal Bayesian regularization (STBR) algorithms for two-dimensional (2-D) normalized cross-correlation (NCC) based multi-level block matching along with incorporation into a Lagrangian cardiac strain estimation framework. Assuming smooth temporal variation over a short span of time, the proposed STBR algorithm performs displacement estimation using at least four consecutive ultrasound radio-frequency (RF) frames by iteratively regularizing 2-D NCC matrices using information from a local spatiotemporal neighborhood in a Bayesian sense. Two STBR schemes are proposed to construct Bayesian likelihood functions termed as Spatial then Temporal Bayesian (STBR-1) and simultaneous Spatiotemporal Bayesian (STBR-2). Radial and longitudinal strain estimated from a finite-element-analysis (FEA) model of realistic canine myocardial deformation were utilized to quantify strain bias, normalized strain error and total temporal relative error (TTR). Statistical analysis with one-way analysis of variance (ANOVA) showed that all Bayesian regularization methods significantly outperform NCC with lower bias and errors (
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