Decreased contractility of the border zone (BZ) myocardium after MI is associated with oxidative stress mediated contractile protein dysfunction. We hypothesized that injection of a thermo-responsive hydrogel with enhanced reactive oxygen species (ROS) scavenging would improve the in vivo contractility of BZ myocardium. To that end, 14 adult male sheep underwent MI. Of those, 6 sheep had a comb-like copolymer synthesized from N-isopropyl acrylamide and 4000 MW methoxy poly(ethylene glycol) methacrylate (NIPAAm-co-PEG4000) injected into the MI zone (MI+Hydrogel). In vivo cardiac magnetic resonance imaging (CMR), including cine DENSE (Displacement encoding with stimulated echoes), was performed before and 6 weeks after MI to measure LV geometry and regional displacement. Tissue ROS and in vitro muscle strip developed force were measured in the BZ and remote regions. Compared to the MI group 6 weeks after MI, MI+Hydrogel exhibited: 1) a reduction in indexed LV end-diastolic (ED) volume; 2) an increase in average wall thickness at ED; and, 3) an increase in average peak circumferential strain in the BZ near the MI border. ROS was significantly lower in the MI+Hydrogel group and muscle strip developed force was significantly increased in the BZ. These observations support the hypothesis that injection of a hydrogel with enhanced ROS scavenging improves in vivo BZ contractility and attenuates ventricular remodeling after MI in sheep. STATEMENT OF SIGNIFICANCE: This work supports our hypothesis that intramyocardial injection of hydrogel with enhanced reactive oxygen species (ROS) scavenging improves in vivo contractility of myocardium bordering the infarct and attenuates pathological ventricular remodeling after myocardial infarction. We believe this multidisciplinary research would be of interest by researchers in the fields of hydrogel biomaterials, finite element biomechanical analysis, and cardiovascular diseases.

IntroductionCurrent mitral annuloplasty rings fail to restrict the anteroposterior distance while allowing dynamic mitral annular changes. We designed and manufactured a mitral annuloplasty ring that demonstrated axis-specific, selective flexibility to meet this clinical need. The objectives were to evaluate ex vivo biomechanics of this ring and to validate the annular dynamics and safety after ring implantation in vivo.MethodsHealthy human mitral annuli (n = 3) were tracked, and motions were isolated. Using the imaging data, we designed and manufactured our axis-specific mitral annuloplasty ring. An ex vivo annular dilation model was used to compare hemodynamics and chordal forces after repair using the axis-specific, rigid, and flexible rings in five porcine mitral valves. In vivo, axis-specific (n = 6), rigid (n = 6), or flexible rings (n = 6) were implanted into male Dorset sheep for annular motion analyses. Five additional animals receiving axis-specific rings survived for up to 6 months.ResultsHere we show the axis-specific, rigid, and flexible rings reduced regurgitation fraction to 4.7 +/- 2.7%, 2.4 +/- 3.2%, and 17.8 +/- 10.0%, respectively. The axis-specific ring demonstrated lower average forces compared to the rigid ring (p = 0.046). Five animals receiving axis-specific rings survived for up to 6 months, with mitral annular motion preserved in vivo. Mature neoendocardial tissue coverage over the device was found to be complete with full endothelialization in all animals.ConclusionsThe axis-specific mitral annuloplasty ring we designed demonstrates excellent capability to repair mitral regurgitation while facilitating dynamic mitral annular motion. This ring has tremendous potential for clinical translatability, representing a promising surgical solution for mitral regurgitation.

Abnormal passive stiffness of the heart muscle (myocardium) is evident in the pathophysiology of several cardiovascular diseases, making it an important indicator of heart health. Recent advancements in cardiac imaging and biophysical modeling now enable more effective evaluation of this biomarker. Estimating passive myocardial stiffness can be accomplished through an MRI-based approach that requires comprehensive subject-specific input data. This includes the gross cardiac geometry (e.g. from conventional cine imaging), regional diastolic kinematics (e.g. from tagged MRI), microstructural configuration (e.g. from diffusion tensor imaging), and ventricular diastolic pressure, whether invasively measured or non-invasively estimated. Despite the progress in cardiac biomechanics simulations, developing a framework to integrate multiphase and multimodal cardiac MRI data for estimating passive myocardial stiffness has remained a challenge. Moreover, the sensitivity of estimated passive myocardial stiffness to input data has not been fully explored. This study aims to: (1) develop a framework for integrating subject-specific in vivo MRI data into in silico left ventricular finite element models to estimate passive myocardial stiffness, (2) apply the framework to estimate the passive myocardial stiffness of multiple healthy subjects under assumed filling pressure, and (3) assess the sensitivity of these estimates to loading conditions and myofiber orientations. This work contributes toward the establishment of a range of reference values for material parameters of passive myocardium in healthy human subjects. Notably, in this study, beat-to-beat variation in left ventricular end-diastolic pressure was found to have a greater influence on passive myocardial material parameter estimation than variation in fiber orientation.

Hypertension is a major risk factor for cardiovascular disease. Pressure-strain loop analysis has recently been introduced as a clinical tool to quantify the pumping efficiency of the left ventricle (LV), as an alternative to the classical pressure-volume loop analysis. The aims of this study were to: (i) combine global longitudinal strain (GLS) from 3D transthoracic echocardiography (TTE) with LV catheter pressure measurements to compute myocardial work indices—namely the global work index (GWI), wasted work index (WWI), and constructive work index (CWI); and (ii) compare work indices between normotensive and hypertensive patients, with further categorisation into medicated and non-medicated sub-groups. 3D TTE and LV pressures were measured in 143 patients (49 females), with 53, 42, 28, and 20 patients in groups of controlled hypertensives, uncontrolled hypertensives, normotensive controls, and untreated hypertensives (e.g., recent diagnoses), respectively. Statistically significant differences (p < 0.05) in GWI and CWI were observed between patients with high blood pressure and the normotensive controls, with largest GWI values in the untreated hypertensive group (1954 ± 322 mmHg

Hypertension is the leading risk factor for heart disease. The use of left ventricular (LV) pressure-strain loop (PSL) analysis has recently emerged for estimating myocardial work. This study sought to: i) derive accurate PSLs using invasive pressure and strain curves from three-dimensional (3D) LV geometry to calculate global work index (GWI), and ii) compare GWI between patients with controlled and uncontrolled hypertension. Invasive LV catheterisation and transthoracic 3D echocardiography (3DE) were performed in 84 patients with controlled (n=30) and uncontrolled (n=54) hypertension. Global longitudinal strain over one cardiac cycle was derived from 3DE using a previously validated algorithm. PSLs were created by matching R-R intervals and aligning pressure and strain curves, and GWI was computed as the area inside the loop. Paired-sample (Welch) t-tests were performed to identify statistically significant differences (p<0.05) between groups. The mean systolic/diastolic pressure was 113/71 mmHg and 135/85 mmHg in patients with controlled and uncontrolled hypertension, respectively. The GWI was significantly higher (p=0.012) in uncontrolled hypertension by 311 mmHg% (mean GWI of 1,521 mmHg% and 1,832 mmHg% in controlled and uncontrolled hypertension groups, respectively). Patients with uncontrolled hypertension exhibit greater myocardial work, likely as a compensatory mechanism to preserve cardiac output against an increased afterload. Pressure-strain loop analysis may provide further insight into the underlying mechanisms of hypertension, with potential implications for therapy and disease management. Future work will explore the incremental prognostic value of GWI for monitoring disease progression.

Right ventricular (RV) volume indices are important prognostic markers in several cardiac diseases. While cardiac magnetic resonance (CMR) imaging remains the gold-standard for volume quantification, echocardiography is more accessible. Unlike two-dimensional echocardiography (2DE), three-dimensional echocardiography (3DE) enables volume quantification without geometric assumptions. However, manual RV segmentation from 3DE is slow and hindered by speckle noise and poor spatial resolution. Machine learning can be utilised to overcome these challenges for efficient and accurate 3DE RV assessment.

BACKGROUND:Left ventricular (LV) global longitudinal strain (GLS) has been proposed as an early imaging biomarker of cardiac mechanical dysfunction. OBJECTIVE:To assess the impact of angiotensin-converting enzyme (ACE) inhibitor treatment of hypertensive heart disease on LV GLS and mechanical function. METHODS:The spontaneously hypertensive rat (SHR) model of hypertensive heart disease ( n  = 38) was studied. A subset of SHRs received quinapril (TSHR, n  = 16) from 3 months (mo). Wistar Kyoto rats (WKY, n  = 13) were used as controls. Tagged cardiac MRI was performed using a 4.7 T Varian preclinical scanner. RESULTS:The SHRs had significantly lower LV ejection fraction (EF) than the WKYs at 3 mo (53.0 ± 1.7% vs. 69.6 ± 2.1%, P  < 0.05), 14 mo (57.0 ± 2.5% vs. 74.4 ± 2.9%, P  < 0.05) and 24 mo (50.1 ± 2.4% vs. 67.0 ± 2.0%, P  < 0.01). At 24 mo, ACE inhibitor treatment was associated with significantly greater LV EF in TSHRs compared to untreated SHRs (64.2 ± 3.4% vs. 50.1 ± 2.4%, P  < 0.01). Peak GLS magnitude was significantly lower in SHRs compared with WKYs at 14 months (7.5% ± 0.4% vs. 9.9 ± 0.8%, P  < 0.05). At 24 months, Peak GLS magnitude was significantly lower in SHRs compared with both WKYs (6.5 ± 0.4% vs. 9.7 ± 1.0%, P  < 0.01) and TSHRs (6.5 ± 0.4% vs. 9.6 ± 0.6%, P  < 0.05). CONCLUSIONS:ACE inhibitor treatment curtails the decline in global longitudinal strain in hypertensive rats, with the treatment group exhibiting significantly greater LV EF and GLS magnitude at 24 mo compared with untreated SHRs.

Atrial fibrillation (AF) is associated with stroke and heart failure, and poses a significant global health burden. Consequently, efforts remain ongoing in better characterising and understanding AF and its underlying mechanisms. This study explores cardiac energetics associated with AF by testing the hypothesis that left ventricular stroke work and systolic power are conserved despite changes in cardiac cycle duration. By combining invasive haemodynamic data and 3D echocardiography, we generated two in vivo pressure-volume loops (corresponding to a short and long cardiac cycle within the same subject) in a sample of 20 patients exhibiting sinus arrhythmia. Subsequently, we found no statistically significant differences in work (0.10 ± 0.22 J) or power (0.03 ± 0.56 W), despite significant differences in stroke volume (7 ± 13 ml) and cardiac output (1.08 ± 0.98 L/min) between short and long cycles (differing by 274 ± 145 ms). Given the repeatability in work and power despite substantial R-R variability, left ventricular energetics may provide more reliable metrics for cardiac function in the presence of AF to better guide patient management.

Diastolic dysfunction of the heart is present in most forms of cardiac failure. Left ventricular (LV) diastolic chamber stiffness has been proposed as a metric for obtaining insights into the progression of this disease and help to inform treatment decisions. However, the challenges in robustly estimating chamber stiffness have limited the evaluation of its prognostic value. This study aimed to develop an automated workflow that enables routine estimation of chamber stiffness from haemodynamic measurements and real-time 3D echocardiographic data to enable such investigations. The workflow was demonstrated on a cohort of 20 patients with heart failure (HF), 7 patients with aortic regurgitation (AR) without HF, and 12 control subjects. A mixed-effects linear regression model was used to examine the differences in diastolic chamber stiffness among the patient groups taking into account the beat-to-beat variations in chamber stiffness estimates. The variances of the standard deviation in chamber stiffness estimates for each patient groups were also evaluated to investigate the influence of beat-to-beat variations in LV pressure on diastolic chamber stiffness estimates. Overall, chamber stiffness was found to be significantly higher int the heart failure with preserved ejection fraction (HFpEF) group (2.4 ± 0.9 mmHg/mL, p = 0.02) and the heart failure with reduced ejection fraction (HFrEF) group (2.1 ± 1.7 mmHg/mL, p = 0.017) compared to the control group (1.1 ± 0.5 mmHg/mL). The lowest estimates were observed in the AR without heart failure group (1 ± 0.4 mmHg/mL, p = 0.84). HFrEF patient group exhibited the largest variance of the standard deviation in chamber stiffness estimates, followed by the HFpEF group, suggesting the beat-to-beat variations in LV pressures had a substantial effect in these groups. Future work will seek to apply this novel automated methodology to support estimation of chamber stiffness in a robust and reproducible manner in larger clinical studies to further elucidate its benefits for patient diagnosis and management.

Segmentation of the left ventricle (LV) in echocardiography is an important task for the quantification of volume and mass in heart disease. Continuing advances in echocardiography have extended imaging capabilities into the 3D domain, subsequently overcoming the geometric assumptions associated with conventional 2D acquisitions. Nevertheless, the analysis of 3D echocardiography (3DE) poses several challenges associated with limited spatial resolution, poor contrast-to-noise ratio, complex noise characteristics, and image anisotropy. To develop automated methods for 3DE analysis, a sufficiently large, labeled dataset is typically required. However, ground truth segmentations have historically been difficult to obtain due to the high inter-observer variability associated with manual analysis. We address this lack of expert consensus by registering labels derived from higher-resolution subject-specific cardiac magnetic resonance (CMR) images, producing 536 annotated 3DE images from 143 human subjects (10 of which were excluded). This heterogeneous population consists of healthy controls and patients with cardiac disease, across a range of demographics. To demonstrate the utility of such a dataset, a state-of-the-art, self-configuring deep learning network for semantic segmentation was employed for automated 3DE analysis. Using the proposed dataset for training, the network produced measurement biases of −9 ± 16 ml, −1 ± 10 ml, −2 ± 5 %, and 5 ± 23 g, for end-diastolic volume, end-systolic volume, ejection fraction, and mass, respectively, outperforming an expert human observer in terms of accuracy as well as scan-rescan reproducibility. As part of the Cardiac Atlas Project, we present here a large, publicly available 3DE dataset with ground truth labels that leverage the higher resolution and contrast of CMR, to provide a new benchmark for automated 3DE analysis. Such an approach not only reduces the effect of observer-specific bias present in manual 3DE annotations, but also enables the development of analysis techniques which exhibit better agreement with CMR compared to conventional methods. This represents an important step for enabling more efficient and accurate diagnostic and prognostic information to be obtained from echocardiography.

Tagged CMR was used to measure torsion and longitudinal strain in a rodent model of hypertensive heart disease. Long-term ACE inhibitor treatment restored ejection fraction, torsion and longitudinal strain by 24 months of age. Longitudinal strain was the first functional measure to be restored, and this may indicate that longitudinal strain is a sensitive imaging biomarker for assessing the efficacy of treatment with regards to reverse remodeling in hypertension.

Elevated left ventricular filling pressure (LVFP) (≥15 mmHg) is a haemodynamic marker of diastolic dysfunction measured invasively during cardiac catheterisation. Attention has recently been drawn to several studies using multivariate regression approaches to identify elevated LVFP using non-invasive imaging. This study investigated the utility of a regression model to estimate LVFP using routine two-dimensional echocardiography (2DE) parameters.

Increased passive myocardial stiffness is implicated in the pathophysiology of many cardiac diseases, and its in vivo estimation can improve management of heart disease. MRI-driven computational constitutive modeling has been used extensively to evaluate passive myocardial stiffness. This approach requires subject-specific data that is best acquired with different MRI sequences: conventional cine (e.g. bSSFP), tagged MRI (or DENSE), and cardiac diffusion tensor imaging. However, due to the lack of comprehensive datasets and the challenge of incorporating multi-phase and single-phase disparate MRI data, no studies have combined in vivo cine bSSFP, tagged MRI, and cardiac diffusion tensor imaging to estimate passive myocardial stiffness. The objective of this work was to develop a personalized in silico left ventricular model to evaluate passive myocardial stiffness by integrating subject-specific geometric data derived from cine bSSFP, regional kinematics extracted from tagged MRI, and myocardial microstructure measured using in vivo cardiac diffusion tensor imaging. To demonstrate the feasibility of using a complete subject-specific imaging dataset for passive myocardial stiffness estimation, we calibrated a bulk stiffness parameter of a transversely isotropic exponential constitutive relation to match the local kinematic field extracted from tagged MRI. This work establishes a pipeline for developing subject-specific biomechanical ventricular models to probe passive myocardial mechanical behavior, using comprehensive cardiac imaging data from multiple in vivo MRI sequences.

Cardiovascular imaging studies provide a multitude of structural and functional data to better understand disease mechanisms. While pooling data across studies enables more powerful and broader applications, performing quantitative comparisons across datasets with varying acquisition or analysis methods is problematic due to inherent measurement biases specific to each protocol. We show how dynamic time warping and partial least squares regression can be applied to effectively map between left ventricular geometries derived from different imaging modalities and analysis protocols to account for such differences. To demonstrate this method, paired real-time 3D echocardiography (3DE) and cardiac magnetic resonance (CMR) sequences from 138 subjects were used to construct a mapping function between the two modalities to correct for biases in left ventricular clinical cardiac indices, as well as regional shape. Leave-one-out cross-validation revealed a significant reduction in mean bias, narrower limits of agreement, and higher intraclass correlation coefficients for all functional indices between CMR and 3DE geometries after spatiotemporal mapping. Meanwhile, average root mean squared errors between surface coordinates of 3DE and CMR geometries across the cardiac cycle decreased from 7 ± 1 to 4 ± 1 mm for the total study population. Our generalised method for mapping between time-varying cardiac geometries obtained using different acquisition and analysis protocols enables the pooling of data between modalities and the potential for smaller studies to leverage large population databases for quantitative comparisons.

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Health Research Council (HRC) of New Zealand and National Heart Foundation (NHF) of New Zealand Introduction—Echocardiographic measures of left ventricular (LV) structure and size, including LV wall thickness and LV end-diastolic dimension (LVID), provide important information in the assessment of patients with heart disease. For example, LV mass is a predictor of outcome for patients with hypertension and LVID is a predictor of cardiac resynchronisation response in patients with heart failure. Advances in 3D echocardiography (3DE) have enabled full-volume acquisitions, which overcome geometric assumptions present in conventional 2D echocardiography (2DE), providing a more accurate representation of cardiac geometry. Although numerous validation studies have been performed for 3DE-derived LV volumes, comparisons of LV dimension by 3DE against established methods are limited. Purpose—We sought to compare routine LV dimension measurements between 3DE and 2DE, with validation using cardiac magnetic resonance (CMR) imaging. Methods—Transthoracic echocardiography (2D and 3D) and cine CMR imaging were performed in 62 prospectively recruited participants (47 healthy controls, 9 patients with LVH, 6 patients with aortic regurgitation), <1 h apart. 2DE LV dimension measurements (interventricular septum [IVS], posterior wall thickness [PWT], and LVID) were taken at end-diastole from the parasternal long axis, and mass was calculated using the linear method based on ASE/EACVI guidelines. For 3DE, 3D geometric models of the LV were constructed by interactively fitting surfaces to the endocardium and epicardium using previously validated software, from which corresponding LV dimension measurements and mass were extracted. Measurements were obtained from CMR by a similar 3D geometric modelling process. Results—Differences (mean ± SD) in LV dimension measurements between the three modalities and intraclass correlation coefficients (ICC) are presented in Table I. When compared with CMR, 3DE exhibited higher agreement in terms of LVID and mass than 2DE, but lower agreement in wall thickness measurements. Statistically significant differences were found between 2DE and 3DE for PWT, LVID, and mass, as well as 2DE and CMR for LVID and mass (where P < 0.01 for a paired sample t­-test, marked with an asterisk). Meanwhile, there were no statistically significant differences between 3DE and CMR for IVS, PWT, LVID, or mass. Conclusions—Our results demonstrate that 3DE is superior to 2DE in terms of LVID and mass quantification, exhibiting good agreement with CMR. 3DE exhibited moderate and poor agreement for IVS and PWT, respectively, with both 2DE and CMR, likely due to the lower spatial resolution of 3DE. Further advances in 3DE image quality and analysis tools are therefore needed to improve accuracy of wall thickness measurements. Since 2DE imaging plane and probe positioning can result in oblique measurement and underestimation of LVID, the assessment of LVID and mass by 3DE is likely to lead to more accurate diagnostic and prognostic outcomes. Abstract Table 1

Abstract Funding Acknowledgements: Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Health Research Council of New Zealand (HRC) National Heart Foundation of New Zealand (NHF) Segmentation of the left ventricular myocardium and cavity in 3D echocardiography (3DE) is a critical task for the quantification of systolic function in heart disease. Continuing advances in 3DE have considerably improved image quality, prompting increased clinical uptake in recent years, particularly for volumetric measurements. Nevertheless, analysis of 3DE remains a difficult problem due to inherently complex noise characteristics, anisotropic image resolution, and regions of acoustic dropout. One of the primary challenges associated with the development of automated methods for 3DE analysis is the requirement of a sufficiently large training dataset. Historically, ground truth annotations have been difficult to obtain due to the high degree of inter- and intra-observer variability associated with manual 3DE segmentation, thus, limiting the scope of AI-based solutions. To address the lack of expert consensus, we instead used labels derived from cardiac magnetic resonance (CMR) images of the same subjects. By spatiotemporally registering CMR labels to corresponding 3DE image data on a per subject basis (Figure 1), we collated 520 annotated 3DE images from a mixed cohort of 130 human subjects (2 independent single-beat acquisitions per subject at end-diastole and end-systole) consisting of healthy controls and patients with acquired cardiac disease. Comprising images acquired across a range of patient demographics, this curated dataset exhibits variation in image quality, 3DE acquisition parameters, as well as left ventricular shape and pose within the 3D image volume. To demonstrate the utility of such a dataset, nn-UNet, a self-configuring deep learning method for semantic segmentation was employed. An 80/20 split of the dataset was used for training and testing, respectively, and data augmentations were applied in the form of scaling, rotation, and reflection. The trained network was capable of reproducing measurements derived from CMR for end-diastolic volume, end-systolic volume, ejection fraction, and mass, while outperforming an expert human observer in terms of accuracy as well as scan-rescan reproducibility (Table I). As part of ongoing efforts to improve the accuracy and efficiency of 3DE analysis, we have leveraged the high resolution and signal-to-noise-ratio of CMR (relative to 3DE), to create a novel, publicly available benchmark dataset for developing and evaluating 3DE labelling methods. This approach not only significantly reduces the effects of observer-specific bias and variability in training data arising from conventional manual 3DE analysis methods, but also improves the agreement between cardiac indices derived from 3DE and CMR. Figure 1. Data annotation workflow Table I. Results