Key result
STBR-2 yields ~2.1-fold higher signal-to-noise ratio in cardiac strain imaging versus standard cross-correlation.
Why the study?
Displacement estimation is a critical processing step to ensure the accuracy and precision of derived strain tensors in cardiac strain imaging for detecting myocardial motion abnormalities.
Does Spatiotemporal Bayesian regularization improve the accuracy and precision of cardiac strain imaging compared to standard normalized cross-correlation?
Does Spatiotemporal Bayesian regularization improve the accuracy and precision of cardiac strain imaging compared to standard normalized cross-correlation?
Spatiotemporal Bayesian regularization algorithms significantly improve the accuracy and signal-to-noise ratio of cardiac strain imaging in simulation and in vivo models.
Improves in vivo cardiac strain imaging in animals; hypothesis-generating and leaves open clinical translation.
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 (p<0.001). However, there was no significant difference among Bayesian methods. For example, mean longitudinal TTR for NCC, SBR, STBR-1 and STBR-2 were 25.41 %, 9.27 %, 10.38 % and 10.13 % respectively An in vivo feasibility study using RF data from ten healthy mice hearts were used to compare the elastographic signal-to-noise ratio (SNRe) calculated using stochastic analysis. STBR-2 had the highest expected SNRe both for radial and longitudinal strain. The mean expected SNRe values for accumulated radial strain for NCC, SBR, STBR-1 and STBR-2 were 5.03, 9.43, 9.42 and 10.58, respectively. Overall results suggest that STBR improves CSI in vivo.
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Mukaddim et al. (2021) studied Healthy myocardial motion (mouse model) (n=10). Spatiotemporal Bayesian regularization (STBR-2) vs. Normalized cross-correlation (NCC) was evaluated on Mean expected elastographic signal-to-noise ratio (SNRe) for accumulated radial strain (p=<0.05). Spatiotemporal Bayesian regularization (STBR-2) significantly improved in vivo cardiac strain imaging quality by increasing the mean expected elastographic signal-to-noise ratio for accumulated radial strain to 10.58 compared to 5.03 with standard normalized cross-correlation.
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