Optimization of Transmit Parameters in Cardiac Strain Imaging With Full and Partial Aperture Coherent Compounding

Preliminary Feasibility of Stress Myocardial Elastography for the Detection of Coronary Artery Disease

Myocardial elastography (ME) is a cardiac strain imaging technique that has been found capable of detecting a decrease in radial strain caused by ischemia or infarction in patients with coronary artery disease (CAD) as well as in a canine model. Prior studies have focused on rest imaging, but stress testing can reveal functional deficits caused by stenoses that are asymptomatic at rest. Therefore, it has been proposed that stress ME (S-ME) improves the detection of CAD. A novel strain difference (Δε) metric is presented and investigated in a canine model of induced ischemia, as well as in a study in human patients with CAD validated by myocardial perfusion imaging. In the canine model study, flow-limiting stenosis was induced by partial ligation in n = 2 canines, and stenosis was found to consistently reduce Δε in the affected myocardial regions compared with baseline, as well as compared to myocardial regions that are remote to the induced stenosis. In the clinical study, the median Δε was significantly lower (p < 0.05) in infarcted myocardial regions (-6.29%) than in those with normal perfusion (4.62%), with Δε in ischemic regions falling in between (-2.91%). The same trend was observed when considering radial strain during stress and, to a lesser degree, at rest alone. The results indicate that S-ME may be more sensitive to mild cases of CAD that are functionally asymptomatic at rest.

Myocardial Strain Imaging With Electrocardiogram-Gated and Coherent Compounding for Early Diagnosis of Coronary Artery Disease

Myocardial elastography (ME) is an ultrasound-based technique that uses radiofrequency signals for 2-D cardiac motion tracking and strain imaging at a high frame rate. Early diagnosis of coronary artery disease (CAD) is critical for timely treatment and improvement of patient outcome. The objective of this study was to assess the performance of ME radial and circumferential strains in the detection and characterization of CAD in patients. In this study, 86 patients suspected of CAD were imaged with ME prior to invasive coronary angiography (ICA). End-systolic radial and circumferential left ventricular strains were estimated in all patients in each of their perfusion territories: left anterior descending (LAD), left circumflex (LCX) and right coronary artery (RCA). ME radial strains were capable of differentiating the obstructive CAD group (55.3 ± 29.8%) from the non-obstructive CAD (72.5 ± 46.8%, p < 0.05) and no CAD groups (73.4 ± 30.4%, p < 0.05) in the RCA territory. ME circumferential strains were capable of differentiating the obstructive CAD group (-3.1 ± 7.5%) from the non-obstructive CAD (-7.2 ± 6.8%, p < 0.05) and normal (-6.9 ± 8.0%, p < 0.05) groups in the LAD territory and to differentiate the normal group (-17.1 ± 8.2%) from the obstructive (-12.8 ± 7.2%, p < 0.05) and non-obstructive CAD (-13.6 ± 8.5%, p < 0.05) groups in the RCA territory. ME circumferential strain performed better than ME radial strain in differentiating normal, non-obstructive and obstructive perfusion territories. In the LCX territory, both ME radial and circumferential strains decreased when the level of stenosis was higher. However, it was not statistically significant. The findings presented herein indicate that ME radial and circumferential estimation obtained from ECG-gated and compounded acquisitions is a promising tool for early, non-invasive and radiation-free detection of CAD in patients.

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.

A New Plane Wave Compounding Scheme Using Phase Compensation for Motion Detection

Plane wave (PW) transmission has enabled multiple new applications, such as shear wave elastography, ultrafast Doppler imaging, and functional ultrasound imaging. PW compounding (PWC), which coherently sums the echo signals from multiple PW transmits with different angles, is widely used to improve B-mode image quality. When the motion between two speckle images is estimated, PWC suffers from an inherent displacement estimation error. This is derived theoretically and experimentally demonstrated in this work. We show that the phase difference between the acquired data with PW emissions with different angles is related to this error. When the absolute value of the phase difference is larger than π /2, the displacement estimation error occurs. A new scheme, named initial-phase-compensated PWC (IPCPWC), is proposed, which compensates the phase of echo signals from each PW transmit and maintains the absolute value of the phase difference smaller than π /2. The increased signal-to-noise ratio and reduced jitter of IPCPWC in motion data are demonstrated using tissue mimicking phantoms compared with PWC.

Monitoring Canine Myocardial Infarction Formation and Recovery via Transthoracic Cardiac Strain Imaging

Myocardial elastography (ME) is an ultrasound-based strain imaging method that aims to determine the degree of ischemia or infarction as a result of the change in the elastic properties of the myocardium. A survival canine model (n = 11) was employed to investigate the ability of ME to image myocardial infarction formation and recovery. Infarcts were generated by ligation of the left anterior descending coronary artery. Canines were survived and imaged for 4 days (n = 7) or 4 weeks (n = 4), allowing sufficient time for recovery via collateral perfusion. A radial strain-based metric, percentage of healthy myocardium by strain (PHMε), was developed as a marker for healthy myocardial tissue. PHMε was strongly linearly correlated with actual infarct size as determined by gross pathology (R2 = 0.80). Mean PHMε was reduced 1-3 days post-infarction (p < 0.05) at the papillary and apical short-axis levels; full infarct recovery was achieved by day 28, with mean PHMε returning to baseline levels. ME was capable of diagnosing individual myocardial segments as non-infarcted or infarcted with high sensitivity (82%), specificity (92%) and precision (85%) (area under the receiver operating characteristic curve = 0.90). The study therefore strengthens the ME premise that it can detect and assess myocardial infarction progression and recovery in vivo and could thus provide an important role in both disease diagnosis and treatment assesssment.

Catheter Ablation Lesion Visualization With Intracardiac Strain Imaging in Canines and Humans

Catheter ablation is a common treatment for arrhythmia, but can fail if lesion lines are noncontiguous. Identification of gaps and nontransmural lesions can reduce the likelihood of treatment failure and recurrent arrhythmia. Intracardiac myocardial elastography (IME) is a strain imaging technique that provides visualization of the lesion line. Estimation of lesion size and gap resolution were evaluated in an open-chest canine model ( n = 3 ), and clinical feasibility was investigated in patients undergoing ablation to treat typical cavotricuspid isthmus (CTI) atrial flutter ( n = 5 ). A lesion line consisting of three lesions and two gaps was generated on the canine left ventricle via epicardial ablation. One lesion was generated in one canine right ventricle. Average lesion and gap areas were measured with high agreement (33 ± 14 and 30 ± 15 mm², respectively) when compared against gross pathology (34 ± 19 and 26 ± 11 mm², respectively). Gaps as small as 11 mm² (3.6 mm on epicardial surface) were identifiable. Absolute error and relative error in estimated lesion area were 9.3 ± 8.4 mm² and 31% ± 34%; error in estimated gap area was 11 ± 9.0 mm² and 40% ± 29%. Flutter patients were imaged throughout the procedure. Strain was shown to be capable of differentiating between baseline and after ablation completion as confirmed by conduction block. In all patients, strain decreased in the CTI after ablation (mean paired difference of -17% ± 11%, ). IME could potentially become a useful ablation monitoring tool in health facilities.

Locally optimized correlation-guided Bayesian adaptive regularization for ultrasound strain imaging

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 _e ) ratios obtained from inclusion phantom indicate improved lesion detectability for both axial and lateral strain images. For instance, at 1.5% lateral strain, Bayesian regularization with five iterations had lateral CNR _e of  -0.31 dB which was significantly increased using the adaptive approach to 7.42 dB (p   <  0.001). Similar results are seen with cardiac deformation modelling with improvement in myocardial strain images. In vivo feasibility was also demonstrated using data from a healthy murine heart. Overall, the proposed method makes Bayesian regularization robust for clinical and preclinical applications.

A comparison between unfocused and focused transmit strategies in cardiac strain imaging

Unfocused ultrasound imaging, particularly coherent compounding with diverging waves, is a commonly employed high-frame rate transmit strategy in cardiac strain imaging. However, the accuracy and precision of diverging wave imaging compared to focused-beam transmit approaches in human subjects is unknown. Three transmit strategies-coherent compounding imaging, composite focused imaging with ECG gating and narrow-beams, and focused imaging with wide-beams-were compared in simulation and in transthoracic imaging of healthy human subjects (n  =  7). The focused narrow-beam sequence estimated radial end-systolic cumulative strains of a simulated left ventricular deformation with 26%  ±  1.5% and 34%  ±  1.5% greater accuracy compared with compounding and wide-beam imaging, respectively. Strain estimation precision in transthoracic imaging was then assessed with the Strain Filter on cumulative end-systolic radial strains. Within the strain values where statistically significant differences in precision (E(SNR_e|ε)) were found between transmit strategies, the narrow-beam sequence estimated radial strain 13%  ±  0.71% and 34%  ±  8.9% more precisely on average compared to compounding or wide-beam imaging, respectively.

3D-rendered Electromechanical Wave Imaging for Localization of Accessory Pathways in Wolff-Parkinson-White Minors

Arrhythmia localization prior to catheter ablation is critical for clinical decision making and treatment planning. The current standard lies in 12-lead electrocardiogram (ECG) interpretation, but this method is non-specific and anatomically limited. Accurate localization requires intracardiac catheter mapping prior to ablation. Electromechanical Wave Imaging (EWI) is a high frame-rate ultrasound modality capable of non-invasively mapping the electromechanical activation in all cardiac chambers in vivo. In this study, we evaluate 3D-rendered EWI as a technique for consistently localizing the accessory pathway (AP) in Wolff-Parkinson-White (WPW) pediatric patients. A 2000 Hz EWI diverging sequence was used to transthoracically image 13 patients with evidence of ECG pre-excitation, immediately prior to catheter ablation and after successful ablation whenever possible. 3D-rendered activation maps were generated by co-registering and interpolating the 4 resulting multi-2D isochrones. A blinded electrophysiologist predicted the AP location on 12-lead ECG prior to ablation. Double-blinded EWI isochrones and clinician assessments were compared to the successful ablation site as confirmed by intracardiac mapping using a segmented template of the heart with 19 ventricular regions. 3D-rendered EWI was shown capable of consistently localizing AP in all the WPW cases. Clinical ECG interpretation correctly predicted the origin with an accuracy of 53.8%, respectively 84.6% when considering predictions in immediately adjacent segments correct. Our method was also capable of assessing the difference in activation pattern from before to after successful ablation on the same patient. These findings indicate that EWI could inform current diagnosis and expedite treatment planning of WPW ablation procedures.

Spatial Angular Compounding With Affine-Model-Based Optical Flow for Improvement of Motion Estimation

Tissue motion estimation is an essential step for ultrasound elastography. Our previous study has shown that the affine-model-based optical flow (OF) method outperforms the normalized cross-correlation-based block matching (BM) method in motion estimation. However, the quality of lateral estimation using OF is still low due to inherent limitation of ultrasound imaging. BM-based spatial angular compounding (SAC) has been developed to obtain better motion estimation. In this paper, OF-based SAC (OF-SAC) is proposed to further improve the performance of lateral (and axial) estimation, and it is compared with BM-based SAC (BM-SAC). Plane wave as well as focused wave is transmitted in both simulations and phantom experiments on a linear array. In order to compare the performance quantitatively, the root-mean-square error (RMSE) of axial/lateral displacement and strain, and signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of axial/lateral strain are used as the evaluation criteria in the simulations. In the phantom experiments, the SNR and CNR are used to assess the quality of axial/lateral strain. The results show that for both OF and BM, SAC improves the performance of motion estimation, regardless of using plane or focused wave transmission. More importantly, OF-SAC is shown to outperform BM-SAC with lower RMSE, higher SNR, and higher CNR. In addition, preliminary in vivo experiments on the carotid artery of a healthy human subject also prove the superiority of OF-SAC. These results suggest that OF-SAC is preferred for both axial and lateral motion estimation to BM-SAC.