Segmental Analysis of Cardiac Short-Axis Views Using Lagrangian Radial and Circumferential Strain

Reproducibility assessment of a biomechanical model-based elasticity imaging method for identifying changes in left ventricular mechanical stiffness

Purpose

Cardiotoxicity of antineoplastic therapies is increasingly a risk to cancer patients treated with curative intent with years of life to protect. Studies highlight the importance of identifying early cardiac decline in cancer patients undergoing cardiotoxic therapies. Accurate tools to study this are a critical clinical need. Current and emerging methods for assessing cardiotoxicity are too coarse for identifying preclinical cardiac degradation or too cumbersome for clinical implementation.

Approach

In the previous work, we developed a noninvasive biomechanical model-based elasticity imaging methodology (BEIM) to assess mechanical stiffness changes of the left ventricle (LV) based on routine cine cardiac magnetic resonance (CMR) images. We examine this methodology to assess methodological reproducibility. We assessed a cohort of 10 participants that underwent test/retest short-axis CMR imaging at baseline and follow-up sessions as part of a previous publicly available study. We compare test images to retest images acquired within the same session to assess within-session reproducibility. We also compare test and retest images acquired at the baseline imaging session to test and retest images acquired at the follow-up imaging session to assess between-session reproducibility.

Results

We establish the within-session and between-session reproducibility of our method, with global elasticity demonstrating repeatability within a range previously demonstrated in cardiac strain imaging studies. We demonstrate increased repeatability of global elasticity compared to segmental elasticity for both within-session and between-session. Within-subject coefficients of variation for within-session test/retest images globally for all modulus directions and a mechanical fractional mechanical stiffness anisotropy metric ranged from 11% to 28%.

Conclusions

Results suggest that our methodology can reproducibly generate estimates of relative mechanical elasticity of the LV and provides a threshold for distinguishing true changes in myocardial mechanical stiffness from experimental variation. BEIM has applications in identifying preclinical cardiotoxicity in breast cancer patients undergoing antineoplastic therapies.

Murine cardiac fibrosis localization using adaptive Bayesian cardiac strain imaging in vivo

An adaptive Bayesian regularized cardiac strain imaging (ABR-CSI) algorithm for in vivo murine myocardial function assessment is presented. We report on 31 BALB/CJ mice (n = 17 females, n = 14 males), randomly stratified into three surgical groups: myocardial infarction (MI, n = 10), ischemia–reperfusion (IR, n = 13) and control (sham, n = 8) imaged pre-surgery (baseline- BL), and 1, 2, 7 and 14 days post-surgery using a high frequency ultrasound imaging system (Vevo 2100). End-systole (ES) radial and longitudinal strain images were used to generate cardiac fibrosis maps using binary thresholding. Percentage fibrotic myocardium (PFM) computed from regional fibrosis maps demonstrated statistically significant differences post-surgery in scar regions. For example, the MI group had significantly higher PFM_Radial (%) values in the anterior mid region (p = 0.006) at Day 14 (n = 8, 42.30 ± 14.57) compared to BL (n = 12, 1.32 ± 0.85). A random forest classifier automatically detected fibrotic regions from ground truth Masson’s trichrome stained histopathology whole slide images. Both PFM_Radial (r = 0.70) and PFM_Longitudinal (r = 0.60) results demonstrated strong, positive correlation with PFM_{Histopathology} (p < 0.001).

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.

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.

Automated analysis of cardiovascular magnetic resonance myocardial native T1 mapping images using fully convolutional neural networks

BACKGROUND

Cardiovascular magnetic resonance (CMR) myocardial native T1 mapping allows assessment of interstitial diffuse fibrosis. In this technique, the global and regional T1 are measured manually by drawing region of interest in motion-corrected T1 maps. The manual analysis contributes to an already lengthy CMR analysis workflow and impacts measurements reproducibility. In this study, we propose an automated method for combined myocardium segmentation, alignment, and T1 calculation for myocardial T1 mapping.

METHODS

A deep fully convolutional neural network (FCN) was used for myocardium segmentation in T1 weighted images. The segmented myocardium was then resampled on a polar grid, whose origin is located at the center-of-mass of the segmented myocardium. Myocardium T1 maps were reconstructed from the resampled T1 weighted images using curve fitting. The FCN was trained and tested using manually segmented images for 210 patients (5 slices, 11 inversion times per patient). An additional image dataset for 455 patients (5 slices and 11 inversion times per patient), analyzed by an expert reader using a semi-automatic tool, was used to validate the automatically calculated global and regional T1 values. Bland-Altman analysis, Pearson correlation coefficient, r, and the Dice similarity coefficient (DSC) were used to evaluate the performance of the FCN-based analysis on per-patient and per-slice basis. Inter-observer variability was assessed using intraclass correlation coefficient (ICC) of the T1 values calculated by the FCN-based automatic method and two readers.

RESULTS

The FCN achieved fast segmentation (< 0.3 s/image) with high DSC (0.85 ± 0.07). The automatically and manually calculated T1 values (1091 ± 59 ms and 1089 ± 59 ms, respectively) were highly correlated in per-patient (r = 0.82; slope = 1.01; p < 0.0001) and per-slice (r = 0.72; slope = 1.01; p < 0.0001) analyses. Bland-Altman analysis showed good agreement between the automated and manual measurements with 95% of measurements within the limits-of-agreement in both per-patient and per-slice analyses. The intraclass correllation of the T1 calculations by the automatic method vs reader 1 and reader 2 was respectively 0.86/0.56 and 0.74/0.49 in the per-patient/per-slice analyses, which were comparable to that between two expert readers (=0.72/0.58 in per-patient/per-slice analyses).

CONCLUSION

The proposed FCN-based image processing platform allows fast and automatic analysis of myocardial native T1 mapping images mitigating the burden and observer-related variability of manual analysis.

Real-Time in Vivo Photoacoustic Imaging in the Assessment of Myocardial Dynamics in Murine Model of Myocardial Ischemia

Photoacoustic imaging (PAI) is an evolving real-time imaging modality that combines the higher contrast of optical imaging with the higher spatial resolution of ultrasound imaging. We utilized dual-wavelength PAI for the diagnosis and monitoring of myocardial ischemia by assessing variations in blood oxygen saturation estimated in a murine model. The use of high-frequency ultrasound in conjunction with PAI enabled imaging of anatomic and functional changes associated with ischemia. Myocardial ischemia was established in eight mice by ligating the left anterior descending artery (LAD). Longitudinal results reveal that PAI is sensitive to acute myocardial ischemia, with a rapid decline in blood oxygen saturation (p ˂ 0.001) observed after LAD ligation (30 min: 33.05 ± 6.80%, 80 min: 36.59 ± 5.22%, 120 min: 36.70 ± 9.46%, 24 h: 40.55 ± 13.04%) compared with baseline (87.83 ± 5.73%). Variation in blood oxygen saturation was found to be linearly correlated with ejection fraction (%), fractional shortening (%) and stroke volume (µL), with Pearson's correlation coefficient values of 0.66, 0.67 and 0.77, respectively (p ˂ 0.001). Our results indicate that PAI has the potential for real-time diagnosis and monitoring of acute myocardial ischemia.

Mathematical Development and Computational Analysis of Harmonic Phase-Magnetic Resonance Imaging (HARP-MRI) Based on Bloch Nuclear Magnetic Resonance (NMR) Diffusion Model for Myocardial Motion

Harmonic Phase-Magnetic Resonance Imaging (HARP-MRI) is a tagged image analysis method that can measure myocardial motion and strain in near real-time and is considered a potential candidate to make magnetic resonance tagging clinically viable. However, analytical expressions of radially tagged transverse magnetization in polar coordinates (which is required to appropriately describe the shape of the heart) have not been explored because the physics required to directly connect myocardial deformation of tagged Nuclear Magnetic Resonance (NMR) transverse magnetization in polar geometry and the appropriate harmonic phase parameters are not yet available. The analytical solution of Bloch NMR diffusion equation in spherical geometry with appropriate spherical wave tagging function is important for proper analysis and monitoring of heart systolic and diastolic deformation with relevant boundary conditions. In this study, we applied Harmonic Phase MRI method to compute the difference between tagged and untagged NMR transverse magnetization based on the Bloch NMR diffusion equation and obtained radial wave tagging function for analysis of myocardial motion. The analytical solution of the Bloch NMR equations and the computational simulation of myocardial motion as developed in this study are intended to significantly improve healthcare for accurate diagnosis, prognosis and treatment of cardiovascular related deceases at the lowest cost because MRI scan is still one of the most expensive anywhere. The analysis is fundamental and significant because all Magnetic Resonance Imaging techniques are based on the Bloch NMR flow equations.

3D Myocardial Elastography In Vivo

Strain evaluation is of major interest in clinical cardiology as it can quantify the cardiac function. Myocardial elastography, a radio-frequency (RF)-based cross-correlation method, has been developed to evaluate the local strain distribution in the heart in vivo. However, inhomogeneities such as RF ablation lesions or infarction require a three-dimensional approach to be measured accurately. In addition, acquisitions at high volume rate are essential to evaluate the cardiac strain in three dimensions. Conventional focused transmit schemes using 2D matrix arrays, trade off sufficient volume rate for beam density or sector size to image rapid moving structure such as the heart, which lowers accuracy and precision in the strain estimation. In this study, we developed 3D myocardial elastography at high volume rates using diverging wave transmits to evaluate the local axial strain distribution in three dimensions in three open-chest canines before and after radio-frequency ablation. Acquisitions were performed with a 2.5 MHz 2D matrix array fully programmable used to emit 2000 diverging waves at 2000 volumes/s. Incremental displacements and strains enabled the visualization of rapid events during the QRS complex along with the different phases of the cardiac cycle in entire volumes. Cumulative displacement and strain volumes depict high contrast between non-ablated and ablated myocardium at the lesion location, mapping the tissue coagulation. 3D myocardial strain elastography could thus become an important technique to measure the regional strain distribution in three dimensions in humans.