2D myocardial deformation imaging based on RF-based non-rigid image registration

An Optimization Approach for Creating Application-specific Ultrasound Speckle Tracking Algorithms

OBJECTIVE

Ultrasound speckle tracking enables in vivo measurement of soft tissue deformation or strain, providing a non-invasive diagnostic tool to quantify tissue health. However, adoption into new fields is challenging since algorithms need to be tuned with gold-standard reference data that are expensive or impractical to acquire. Here, we present a novel optimization approach that only requires repeated measurements, which can be acquired for new applications where reference data might not be readily available or difficult to get hold of.

METHODS

Soft tissue motion was captured using ultrasound for the medial collateral ligament (MCL) of three quasi-statically loaded porcine stifle joints, and medial ligamentous structures of a dynamically loaded human cadaveric knee joint. Using a training subset, custom speckle tracking algorithms were created for the porcine and human ligaments using surrogate optimization, which aimed to maximize repeatability by minimizing the normalized standard deviation of calculated strain maps for repeat measurements. An unseen test subset was then used to validate the tuned algorithms by comparing the ultrasound strains to digital image correlation (DIC) surface strains (porcine specimens) and length change values of the optically tracked ligament attachments (human specimens).

RESULTS

After 1500 iterations, the optimization routine based on the porcine and human training data converged to similar values of normalized standard deviations of repeat strain maps (porcine: 0.19, human: 0.26). Ultrasound strains calculated for the independent test sets using the tuned algorithms closely matched the DIC measurements for the porcine quasi-static measurements (R > 0.99, RMSE < 0.59%) and the length change between the tracked ligament attachments for the dynamic human dataset (RMSE < 6.28%). Furthermore, strains in the medial ligamentous structures of the human specimen during flexion showed a strong correlation with anterior/posterior position on the ligaments (R > 0.91).

CONCLUSION

Adjusting ultrasound speckle tracking algorithms using an optimization routine based on repeatability led to robust and reliable results with low RMSE for the medial ligamentous structures of the knee. This tool may be equally beneficial in other soft-tissue displacement or strain measurement applications and can assist in the development of novel ultrasonic diagnostic tools to assess soft tissue biomechanics.

Spatiotemporal Bayesian Regularization for Cardiac Strain Imaging: Simulation and In Vivo Results

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 (SNR_e) calculated using stochastic analysis. STBR-2 had the highest expected SNR_e both for radial and longitudinal strain. The mean expected SNR_e 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.

Bayesian Regularized Strain Imaging for Assessment of Murine Cardiac Function In vivo

A cardiac strain imaging framework with adaptive Bayesian regularization (ABR) is proposed for in vivo assessment of murine cardiac function. The framework uses ultrasound (US) radio-frequency data collected with a high frequency (f_c = 30MHz) imaging system and a multi-level block matching algorithm with ABR to derive inter-frame cardiac displacements. Lagrangian cardiac strain (radial, e_r and longitudinal, e_l) tensors were derived by segmenting the myocardial wall starting at the ECG R-wave and accumulating interframe deformations over a cardiac cycle. In vivo feasibility was investigated through a longitudinal study with two mice (one ischemia-perfusion (IR) injury and one sham) imaged at five sessions (pre-surgery (BL) and 1,2,7 and 14 days post-surgery). End-systole (ES) strain images and segmental strain curves were derived for quantitative evaluation. Both mice showed periodic variation of e_r and e_l strain at BL with segmental synchroneity. Infarcted regions of IR mouse at Day 14 were associated with reduced or sign reversed ES e_r and e_l values while the sham mouse had similar or higher strain than at BL. Infarcted regions identified in vivo were associated with increased collagen content confirmed with Masson's Trichrome stained ex vivo heart sections.Clinical Relevance-Higher quality cardiac strain images derived with RF data and Bayesian regularization can potentially improve the sensitivity and accuracy of non-invasive assessment of cardiovascular disease models.

In Vivo Comparison of Multiline Transmission and Diverging Wave Imaging for High-Frame-Rate Speckle-Tracking Echocardiography

High-frame-rate (HFR) speckle-tracking echocardiography (STE) assesses myocardial function by quantifying motion and deformation at high temporal resolution. Among the proposed HFR techniques, multiline transmission (MLT) and diverging wave (DW) imaging have been used in this context both being characterized by specific advantages and disadvantages. Therefore, in this article, we directly contrast both approaches in an in vivo setting while operating at the same frame rate (FR). First, images were recorded at baseline (resting condition) from healthy volunteers and patients. Next, additional acquisitions during stress echocardiography were performed on volunteers. Each scan was contoured and processed by a previously proposed 2-D HFR STE algorithm based on cross correlation. Then, strain curves and their end-systolic (ES) values were extracted for all myocardial segments for further statistical analysis. The baseline acquisitions did not reveal differences in estimated strain between the acquisition modes ( ); myocardial segments ( ); or an interaction between imaging mode and depth ( ). Similarly, during stress testing, no difference ( p = 0.7 ) was observed for the two scan sequences, stress levels or an interaction sequence-stress level ( p = 0.94 ). Overall, our findings show that MLT and DW compoundings give comparable HFR STE strain values and that the choice for using one method or the other may thus rather be based on other factors, for example, system requirements or computational cost.

Parameterized Strain Estimation for Vascular Ultrasound Elastography With Sparse Representation

Ultrasound vascular strain imaging has shown its potential to interrogate the motion of the vessel wall induced by the cardiac pulsation for predicting plaque instability. In this study, a sparse model strain estimator (SMSE) is proposed to reconstruct a dense strain field at a high resolution, with no spatial derivatives, and a high computation efficiency. This sparse model utilizes the highly-compacted property of discrete cosine transform (DCT) coefficients, thereby allowing to parameterize displacement and strain fields with truncated DCT coefficients. The derivation of affine strain components (axial and lateral strains and shears) was reformulated into solving truncated DCT coefficients and then reconstructed with them. Moreover, an analytical solution was derived to reduce estimation time. With simulations, the SMSE reduced estimation errors by up to 50% compared with the state-of-the-art window-based Lagrangian speckle model estimator (LSME). The SMSE was also proven to be more robust than the LSME against global and local noise. For in vitro and in vivo tests, residual strains assessing cumulated errors with the SMSE were 2 to 3 times lower than with the LSME. Regarding computation efficiency, the processing time of the SMSE was reduced by 4 to 25 times compared with the LSME, according to simulations, in vitro and in vivo results. Finally, phantom studies demonstrated the enhanced spatial resolution of the proposed SMSE algorithm against LSME.

An optimisation-based iterative approach for speckle tracking echocardiography

Speckle tracking is the most prominent technique used to estimate the regional movement of the heart based on echocardiograms. In this study, we propose an optimised-based block matching algorithm to perform speckle tracking iteratively. The proposed technique was evaluated using a publicly available synthetic echocardiographic dataset with known ground-truth from several major vendors and for healthy/ischaemic cases. The results were compared with the results from the classic (standard) two-dimensional block matching. The proposed method presented an average displacement error of 0.57 pixels, while classic block matching provided an average error of 1.15 pixels. When estimating the segmental/regional longitudinal strain in healthy cases, the proposed method, with an average of 0.32 ± 0.53, outperformed the classic counterpart, with an average of 3.43 ± 2.84. A similar superior performance was observed in ischaemic cases. This method does not require any additional ad hoc filtering process. Therefore, it can potentially help to reduce the variability in the strain measurements caused by various post-processing techniques applied by different implementations of the speckle tracking. Graphical Abstract Standard block matching versus proposed iterative block matching approach.

Hierarchical Motion Estimation With Bayesian Regularization in Cardiac Elastography: Simulation and In Vivo Validation

Cardiac elastography (CE) is an ultrasound-based technique utilizing radio-frequency (RF) signals for assessing global and regional myocardial function. In this work, a complete strain estimation pipeline for incorporating a Bayesian regularization-based hierarchical block-matching algorithm, with Lagrangian motion description and myocardial polar strain estimation is presented. The proposed regularization approach is validated using finite-element analysis (FEA) simulations of a canine cardiac deformation model that is incorporated into an ultrasound simulation program. Interframe displacements are initially estimated using a hierarchical motion estimation framework. Incremental displacements are then accumulated under a Lagrangian description of cardiac motion from end-diastole (ED) to end-systole (ES). In-plane Lagrangian finite strain tensors are then derived from the accumulated displacements. Cartesian to cardiac coordinate transformation is utilized to calculate radial and longitudinal strains for ease of interpretation. Benefits of regularization are demonstrated by comparing the same hierarchical block-matching algorithm with and without regularization. Application of Bayesian regularization in the canine FEA model provided improved ES radial and longitudinal strain estimation with statistically significant ( ) error reduction of 48.88% and 50.16%, respectively. Bayesian regularization also improved the quality of temporal radial and longitudinal strain curves with error reductions of 78.38% and 86.67% ( ), respectively. Qualitative and quantitative improvements were also visualized for in vivo results on a healthy murine model after Bayesian regularization. Radial strain elastographic signal-to-noise ratio (SNR_e) increased from 3.83 to 4.76 dB, while longitudinal strain SNR_e increased from 2.29 to 4.58 dB with regularization.