End-diastolic Phase Research Articles

Impact of epicardial adipose tissue and atrial functional impairment in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement

Abstract Background Recent studies outlined the additional prognostic value of atrial function in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR). At the same time, increased epicardial adipose tissue (EAT) volumes have proven impact on atrial functional impairment in cardiovascular disease by nurturing adverse atrial remodelling. Purpose To explore the impact of increased EAT volumes as a potential pathomechanistic factor of atrial functional impairment in patients with severe AS undergoing TAVR. Methods 146 patients with severe AS underwent cardiac magnetic resonance (CMR) within 48h prior to TAVR between January 2017 and June 2021. Image analyses included myocardial volume quantification as well as CMR-feature-tracking derived strain. EAT volumes were manually delineated on short axis stacks in end-diastolic cardiac phases and indexed to body surface area. Functional and morphological parameters were compared between patients with high and low EAT volumes. Associations between morphological and functional parameters and EAT were tested using a multivariable linear regression model including patient specific parameters. Results After dichotomization at the median of 46.5ml/m2 total EAT volume, patients were assigned to high- and low-EAT groups accordingly. There was comparable age and body mass index and similar rates of co-morbidities such as hypertension, diabetes and atrial fibrillation in both groups. We observed a higher number of male patients in the high-EAT group (36 [53%] vs. 50 [72%], p=0.018). Regarding left and right ventricular dimensions and function, no differences could be shown. Left atrial (LA) end-systolic volumes were significantly higher in the high-EAT group (29.4ml/ml² [19.7-39.4] vs. 40.9ml/ml² [30.2-59.3], p=0.008), while there were no significant differences in end-diastolic volumes. Furthermore, LA reservoir strain was significantly lower in the high-EAT group compared to the low-EAT group (18.0% [11.7-21.0] vs. 11.8% [7.7-16.4], p=0.008) and was independently associated with higher EAT volumes (p=0.018) irrespective of cardiovascular risk factors and left ventricular (LV) systolic function in a multivariable regression model. Conclusion In patients with severe AS undergoing TAVR, increased EAT volumes are associated with both, morphological and functional changes of the atria. As impaired atrial function was previously shown to be an important predictor of mortality in TAVR patients, those results potentially render EAT as an additional pathomechanistic link between atrial remodelling and worse outcome in TAVR patients.

Impaired strain in diabetic patients with stable coronary artery disease assessed by cardiac magnetic resonance

Abstract Background Type 2 diabetes mellitus (T2DM) can contribute or lead to myocardial structural derangement by multiple mechanisms, including ischemic cardiomyopathy and, more rarely and of difficult evaluation, diabetic cardiomyopathy. Early stages of myocardial dysfunction can be assessed by strain analysis with cardiac magnetic resonance feature tracking (CMR-FT), however, there is no studies that examined deformation parameters in diabetic patients with stablished coronary artery disease (CAD). Purpose We sought to compare the strain parameters of patients with and without T2DM with stable CAD. Methods Patients with stable multivessel CAD and preserved left ventricular ejection fraction (LVEF), included in the MASS V trial, underwent contrast-enhanced cardiac magnetic resonance before revascularization procedures. Patients were stratified according to T2DM diagnosis at baseline. Feature tracking was performed using short-axis cine images and 2- and 4-chamber long-axis images. The left ventricular endocardial and epicardial contours of the end diastole phase were manually delineated in all images. Automatically tracking contours were performed to calculate left ventricular global longitudinal (LVGLS), circumferential (LVGCS) and radial (LVGRS) strain (FIGURE 1). Results Of 202 patients studied, 88 (44%) were diabetic and 114 (56%) non-diabetic. Baseline characteristics were similar between groups (age 70 ± 10 vs 69 ± 11; 69% vs 68% males; LVEF 65 ± 13 vs 67 ± 9). Mean Syntax score was 21.2 ± 8.5 vs 20.4 ± 8.5 (P=0.52) in diabetic and non-diabetic, respectively. There were no differences in left ventricle volumes, mass and LVEF. Mean LGE mass was 2.3 ± 0.9 vs 1.9 ± 0.9 grams (P=0.32) respectively in T2DM and control groups. Strain values were all reduced in T2DM patients, as shown in FIGURE 2 (LVGLS: -15.5% ± 2.3% vs -17.5% ± 2.7%, P=0.02; LVGCS: -15.4% ± 2.6% vs -17.4% ± 3.0%, P=0.03; LVGCS: 25.9% ± 5.9% vs 29.5% ± 7.2%, P=0.01). Multivariable analyses adjusted for age, sex, BMI, hypertension, Syntax score and LGE mass showed no differences in the results. Conclusion In this study, T2DM was associated with impaired deformation parameters (decreased left ventricular global longitudinal, circumferential, and radial strain). These findings suggest myocardial dysfunction in diabetic patients when compared to control subjects in the setting of stable multivessel CAD.FIGURE 1FIGURE 2

In vitro quantification of stent-graft behaviour during chimney thoracic endovascular aortic repair.

To quantify dynamic gutter phenomena and endograft deformations during double chimney thoracic endovascular aortic repair (ch-TEVAR) in a physiological model of the thoracic aorta subjected to pulsatile haemodynamic conditions. Two in vitro procedures revascularizing the brachiocephalic trunk and left common carotid artery were performed representing both balloon-expandable (BE, Ankura-BeGraft) and self-expandable (SE, Ankura-Viabahn) double ch-TEVAR configurations. Retrospectively gated computed tomography (CT) was used to evaluate endograft behaviour. Device interactions were characterised according to gutter volume, gutter surface deviation, and endograft deformation (D-ratio) at end-diastolic and peak-systolic aortic pressure. Use of BE chimney grafts resulted in three times total gutter volume compared to SE chimney grafts. Gutter volumes were observed to vary dynamically between the end-diastolic and peak-systolic phases of the cardiac cycle, with the most substantial change associated with the BE configuration. Chimney graft deformations were dependent on device type, with SE devices exhibiting up to twice the deformation as BE devices. When adjacent, SE chimney grafts were observed to support each other, and thus tended towards a more consistently circular shape. Gutter and chimney graft behaviour were dependent on device type, and exhibited both spatial and temporal variability. This study emphasises notable differences between BE and SE double ch-TEVAR configurations which should be considered when evaluating risk of endoleak. The findings reported here also support the use of gated CT to better identify device-related complications with ch-TEVAR, and can be used in the design of next generation devices.

A study on the application of radiomics based on cardiac MR non-enhanced cine sequence in the early diagnosis of hypertensive heart disease

BackgroundThe prevalence of hypertensive heart disease (HHD) is high and there is currently no easy way to detect early HHD. Explore the application of radiomics using cardiac magnetic resonance (CMR) non-enhanced cine sequences in diagnosing HHD and latent cardiac changes caused by hypertension.Methods132 patients who underwent CMR scanning were divided into groups: HHD (42), hypertension with normal cardiac structure and function (HWN) group (46), and normal control (NOR) group (44). Myocardial regions of the end-diastolic (ED) and end-systolic (ES) phases of the CMR short-axis cine sequence images were segmented into regions of interest (ROI). Three feature subsets (ED, ES, and ED combined with ES) were established after radiomic least absolute shrinkage and selection operator feature selection. Nine radiomic models were built using random forest (RF), support vector machine (SVM), and naive Bayes. Model performance was analyzed using receiver operating characteristic curves, and metrics like accuracy, area under the curve (AUC), precision, recall, and specificity.ResultsThe feature subsets included first-order, shape, and texture features. SVM of ED combined with ES achieved the highest accuracy (0.833), with a macro-average AUC of 0.941. AUCs for HHD, HWN, and NOR identification were 0.967, 0.876, and 0.963, respectively. Precisions were 0.972, 0.740, and 0.826; recalls were 0.833, 0.804, and 0.863, respectively; and specificities were 0.989, 0.863, and 0.909, respectively.ConclusionsRadiomics technology using CMR non-enhanced cine sequences can detect early cardiac changes due to hypertension. It holds promise for future use in screening for latent cardiac damage in early HHD.

Comparison and Agreement between Cardiovascular Computed Tomography-Derived Mid-Diastolic and End-Diastolic Ventricular Volume in Patients with Congenital Heart Disease.

Prospective electrocardiogram (ECG)-triggered cardiovascular computed tomography (CCT) is primarily utilized for anatomical information in congenital heart disease (CHD) and has not been utilized for calculation of the end-diastolic volume (EDV); however, the mid-diastolic volume (MDV) may be measured. The objective of this study was to evaluate the feasibility and agreement between ventricular EDV and MDV. 31 retrospectively ECG-gated CCT were analyzed for the study of the 450 consecutive CCT. CCT images were processed using syngo.via with automatic contouring followed by manual adjustment of the endocardial borders of the left ventricles (LV) and right ventricles (RV) at end-diastolic and mid-diastolic phase (measured at 70% of cardiac cycle). The correlation and agreements between EDV and MDV were demonstrated using Spearman rank coefficient and intraclass correlation coefficient (ICC), respectively. Mean age ± SD was 28.8 ± 12.5 years, 19 were male (61.3%) and tetralogy of Fallot (TOF) was the most common diagnosis (58.1%), 35% (11/31) patients with a pacemaker, ICD or other such contraindication for a CMRI, 23% (7/31) with claustrophobia, and 6.5% (2/31) with developmental delay with refusal for sedation did not have a previous CMRI. The mean ± SD indexed LV EDV and LV MDV were 91.1 ± 24.5 and 84.8 ± 22.3 ml/m2, respectively. The mean ± SD indexed RV EDV and RV MDV were 136.8 ± 41 and 130.2 ± 41.5 ml/m2, respectively. EDV and MDV had a strong positive correlation and good agreement (ICC 0.92 for LV and 0.95 for RV). This agreement was preserved in a subset of patients (21) with dilated RV (indexed RV EDV z-score > 2). Intra-observer reliability (0.97 and 0.98 for LV and RV MDV, respectively) and inter-observer reliability (0.96 and 0.90 for LV and RV MDV, respectively) were excellent. In a select group of patients with CHD, measuring MDV by CCT is feasible and these values have good agreements with EDV. This may be used to derive functional data from prospectively ECG-triggered CCT studies. Further large-scale analysis is needed to determine accuracy and clinical correlation.

Assessment of deep learning segmentation for real-time free-breathing cardiac magnetic resonance imaging at rest and under exercise stress

In recent years, a variety of deep learning networks for cardiac MRI (CMR) segmentation have been developed and analyzed. However, nearly all of them are focused on cine CMR under breathold. In this work, accuracy of deep learning methods is assessed for volumetric analysis (via segmentation) of the left ventricle in real-time free-breathing CMR at rest and under exercise stress. Data from healthy volunteers (n = 15) for cine and real-time free-breathing CMR at rest and under exercise stress were analyzed retrospectively. Exercise stress was performed using an ergometer in the supine position. Segmentations of two deep learning methods, a commercially available technique (comDL) and an openly available network (nnU-Net), were compared to a reference model created via the manual correction of segmentations obtained with comDL. Segmentations of left ventricular endocardium (LV), left ventricular myocardium (MYO), and right ventricle (RV) are compared for both end-systolic and end-diastolic phases and analyzed with Dice’s coefficient. The volumetric analysis includes the cardiac function parameters LV end-diastolic volume (EDV), LV end-systolic volume (ESV), and LV ejection fraction (EF), evaluated with respect to both absolute and relative differences. For cine CMR, nnU-Net and comDL achieve Dice’s coefficients above 0.95 for LV and 0.9 for MYO, and RV. For real-time CMR, the accuracy of nnU-Net exceeds that of comDL overall. For real-time CMR at rest, nnU-Net achieves Dice’s coefficients of 0.94 for LV, 0.89 for MYO, and 0.90 for RV and the mean absolute differences between nnU-Net and the reference are 2.9 mL for EDV, 3.5 mL for ESV, and 2.6% for EF. For real-time CMR under exercise stress, nnU-Net achieves Dice’s coefficients of 0.92 for LV, 0.85 for MYO, and 0.83 for RV and the mean absolute differences between nnU-Net and reference are 11.4 mL for EDV, 2.9 mL for ESV, and 3.6% for EF. Deep learning methods designed or trained for cine CMR segmentation can perform well on real-time CMR. For real-time free-breathing CMR at rest, the performance of deep learning methods is comparable to inter-observer variability in cine CMR and is usable for fully automatic segmentation. For real-time CMR under exercise stress, the performance of nnU-Net could promise a higher degree of automation in the future.

Clinical impact of the right ventricular impairment in patients following transcatheter aortic valve replacement

The right ventricular (RV) impairment can predict clinical adverse events in patients following transcatheter aortic valve replacement (TAVR) for severe aortic stenosis (AS). Limited reports have compared impact of the left ventricular (LV) and RV disorders. This retrospective study evaluated two-year major adverse cardiac and cerebrovascular events (MACCE) in patients following TAVR for severe AS. RV sphericity index was calculated as the ratio between RV mid-ventricular and longitudinal diameters during the end-diastolic phase. Of 239 patients, 2-year MACCE were observed in 34 (14%). LV ejection fraction was 58 ± 11%. Tricuspid annular plane systolic excursion (TAPSE) and RV sphericity index were 20 ± 3 mm and 0.36 (0.31–0.39). Although the univariate Cox regression analysis demonstrated that both LV and RV parameters predicted the outcomes, LV parameters no longer predicted them after adjustment. Lower TAPSE (adjusted hazard ratio per 1 mm, 0.84; 95% confidence interval, 0.75–0.93) and higher RV sphericity index (adjusted hazard ratio per 0.1, 1.94; 95% confidence interval, 1.17–3.22) were adverse clinical predictors. In conclusion, the RV structural and functional disorders predict two-year MACCE, whereas the LV parameters do not. Impact of LV impairment can be attenuated after development of RV disorders.

Automatic segmentation of 2D echocardiography ultrasound images by means of generative adversarial network.

Automated cardiac segmentation from two-dimensional (2D) echocardiographic images is a crucial step toward improving clinical diagnosis. Anatomical heterogeneity and inherent noise, however, present technical challenges and lower segmentation accuracy. The objective of this study is to propose a method for the automatic segmentation of the ventricular endocardium, the myocardium, and the left atrium, in order to accurately determine clinical indices. Specifically, we suggest using the recently introduced pixel-to-pixel Generative Adversarial Network (Pix2Pix GAN) model for accurate segmentation. To accomplish this, we integrate the backbone PatchGAN model for the discriminator and the UNET for the generator, for building the Pix2Pix GAN. The resulting model produces precisely segmented images, thanks to UNET's capability for precise segmentation and PatchGAN's capability for fine-grained discrimination. For the experimental validation, we use the Cardiac Acquisitions for Multi-structure Ultrasound Segmentation (CAMUS) dataset, which consists of echocardiographic images from 500 patients in 2-chamber (2CH) and 4-chamber (4CH) views at the end-diastolic (ED) and end-systolic (ES) phases. Similarly to state-of-the-art studies on the same dataset, we followed the same train-test splits. Our results demonstrate that the proposed GAN-based technique improves segmentation performance for clinical and geometrical parameters compared to the state-of-the-art methods. More precisely, throughout the ED and ES phases, the mean Dice values for the left ventricular endocardium reached 0.961 and 0.930 for 2CH, and 0.959 and 0.950 for 4CH, respectively. Furthermore, the average ejection fraction correlation and Mean Absolute Error obtained were 0.95 and 3.2ml for 2CH, and 0.98 and 2.1ml for 4CH, outperforming the state-of-the-art results.

Optical Flow-Guided Cine MRI Segmentation With Learned Corrections.

In cardiac cine magnetic resonance imaging (MRI), the heart is repeatedly imaged at numerous time points during the cardiac cycle. Frequently, the temporal evolution of a certain region of interest such as the ventricles or the atria is highly relevant for clinical diagnosis. In this paper, we devise a novel approach that allows for an automatized propagation of an arbitrary region of interest (ROI) along the cardiac cycle from respective annotated ROIs provided by medical experts at two different points in time, most frequently at the end-systolic (ES) and the end-diastolic (ED) cardiac phases. At its core, a 3D TV- L1 -based optical flow algorithm computes the apparent motion of consecutive MRI images in forward and backward directions. Subsequently, the given terminal annotated masks are propagated by this bidirectional optical flow in 3D, which results, however, in improper initial estimates of the segmentation masks due to numerical inaccuracies. These initially propagated segmentation masks are then refined by a 3D U-Net-based convolutional neural network (CNN), which was trained to enforce consistency with the forward and backward warped masks using a novel loss function. Moreover, a penalization term in the loss function controls large deviations from the initial segmentation masks. This method is benchmarked both on a new dataset with annotated single ventricles containing patients with severe heart diseases and on a publicly available dataset with different annotated ROIs. We emphasize that our novel loss function enables fine-tuning the CNN on a single patient, thereby yielding state-of-the-art results along the complete cardiac cycle.

Reference intervals for carotid artery intima-media thickness measurements in the multi-ethnic Asian population: relationship with cardiometabolic risk factors

Abstract Background Carotid artery intima-media thickness (cIMT) is an intermediate surrogate marker for cardiovascular disease (CVD). It has been reported that although the distribution of cIMT values is very heterogeneous and values vary widely between different ethnicity, geographic location, and ultrasound protocol, the prediction of vascular events with study-specific cIMT values is consistent across all ethnicities and regions (1). Hence, Consensus Statement from the American Society of Echocardiography suggests that cIMT greater than or equal to 75th percentile for the patient’s age, sex, and ethnicity is indicative of increased CVD risk (2). Aims We aimed to establish age, sex, and ethnic-specific reference intervals in the multi-ethnic Asian population comprising Chinese, Malays, and Indians. Methods We analysed data from 8762 healthy individuals who had a 3D carotid ultrasonography done as part of the Health for Life in Singapore study. Carotid ultrasonography measurements were taken from the distal common carotid artery (CCA) at both the lateral and posterior angles bilaterally during the end-diastolic phase. The mean of all 4 measurements were taken as average cIMT (avgcIMT). For this analysis, we excluded individuals on treatment for diabetes, hypertension, hyperlipidemia, and ischemic heart disease to derive a healthy population subset comprising of 6522 individuals (Chinese: 69%, Malays: 14%, Indians: 17%). We then assessed the 75th percentile as a marker of cardiovascular risk in all individuals, by testing associations with Framingham risk scores (locally adapted) and with cardiometabolic risk factors (3). Results Ethnic- and sex-specific 75th percentiles (95% CI) of CIMT for our Asian populations are summarised in Table 1. Compared to reference intervals reported in European and African American populations, the 75th centiles are lower in all Asian ethnic subgroups (p<0.05). Framingham risk score, BMI, LDL-C, HbA1c and systolic blood pressure were higher in Asians with cIMT more than 75th percentile (p<0.001), compared to Asians with normal cIMT. Conclusions We estimated race- and sex-specific 75th percentiles of avgcIMT across age in the multi-ethnic Asian population. The thresholds for Asian populations are substantially lower than reported in European and African populations. The 75th centiles identify people with raised cardiovascular risk factors, supporting the validity of cIMT as marker of vascular risk in the population. The 75th centiles will be useful in interpretation of cIMT measures obtained both in research and clinical settings in different ethnic populations in Asia. Longitudinal evaluation is needed to determine the prediction of our Asian specific thresholds for predicting hard cardiovascular outcomes.

Incremental prognostic value of fully automatic LVEF measured at stress using machine learning

Abstract Background Cardiovascular magnetic resonance (CMR) is the gold standard to measure left ventricular ejection fraction (LVEF), and novel artificial intelligence-based automatic analyses have been proposed for less user interaction and time saving. However, whether automatic LVEF delivers similar information for risk stratification remains unknown. Purpose To assess the prognostic value for all-cause mortality of LVEF measured by stress CMR using a fully automatic machine learning algorithm without human correction. Methods Between 2016 and 2018, all consecutive patients referred for vasodilator stress CMR were included and followed for the occurrence of all-cause death. A fully automatic machine learning algorithm was trained on 3,700 scans and validated on 1,719 unseen CMR studies (MAGNETOM Aera and Skyra, Siemens Healthcare, Erlangen, Germany) to identify end-diastolic and end-systolic phases and segment LV volumes from short-axis cine images at stress. The algorithm combines multiple deep learning networks for detection and segmentation with an active contours approach. Manual and automatic LVEF measured at stress were compared with the paired Wilcoxon test, Pearson correlation and Bland-Altman analysis. Cox regressions were performed to determine the prognostic value of automatic LVEF measured at stress. Results Among 9,883 included patients included to this study, the automatic LVEF was successfully computed in 9,712 (98.3%) patients (66.6% male, mean age 66±12 years). The agreement between manual and automatic LVEF was good (bias = -0.01%, 95% limits of agreement, -6.7% to 6.7%; Pearson’s correlation coefficient r=0.94). A total of 504 (5.2%) deaths were observed during a median (IQR) follow-up period of 4.5 (3.7-5.2) years. Both manual and automatic volumetric assessments showed similar impact on outcome in univariate analyses (manual LVEF per 5%: hazard ratio [HR], 0.80 [95% CI 0.77–0.83]; p<0.001; automatic LVEF per 5%: HR, 0.84 [95% CI, 0.81–0.86]; p<0.001) and multivariable analyses (manual LVEF per 5%: HR, 0.81 [95% CI, 0.78–0.84]; p<0.001; automatic LVEF per 5%: HR, 0.84 [95% CI, 0.82–0.87]; p<0.001). Fully automatic stress LVEF showed an incremental prognostic value to predict all-cause mortality above all traditional risk factors, LVEF measured at rest, the presence of inducible ischemia and LGE (C-statistic improvement: 0.04; NRI=0.221; IDI=0.049; all p<0.001). Conclusions Automatic LVEF is equally predictive of all-cause mortality compared to manual LVEF, and has an incremental prognostic value compared to traditional risk factors, and other stress CMR parameters. Figures: Figure 1: Linear regression plots (A) and Bland-Altman plots (B) comparing stress LVEF measured by experts (LVEFexpert) and computed by fully-automated artificial-intelligence algorithms (LVEFAI). Figure 2: Kaplan-Meier curves for MACE stratified by fully-automated LVEF value. Test comparing the three groups is based on the log-rank test.

Non-contrast cine cardiovascular magnetic resonance-based radiomics nomogram for predicting microvascular obstruction after reperfusion in ST-segment elevation myocardial infarction.

This study aimed to develop and validate a cine cardiovascular magnetic resonance (CMR)-based radiomics nomogram model for predicting microvascular obstruction (MVO) following reperfusion in patients with ST-segment elevation myocardial infarction (STEMI). In total, 167 consecutive STEMI patients were retrospectively enrolled. The patients were randomly divided into training and validation cohorts with a ratio of 7:3. All patients were diagnosed with myocardial infarction with or without MVO based on late gadolinium enhancement imaging. Radiomics features were extracted from the cine CMR end-diastolic volume phase of the entire left ventricular myocardium (3D volume). The least absolute shrinkage and selection operator (LASSO) regression was employed to select the features that were most relevant to the MVO; these features were then used to calculate the radiomics score (Rad-score). A combined model was developed based on independent risk factors screened using multivariate regression analysis and visualized using a nomogram. Performance was assessed using receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA). The univariate analysis of clinical features demonstrated that only cardiac troponin I (cTNI) was significantly associated with MVO. LASSO regression revealed that 12 radiomics features were strongly associated with MVO. Multivariate regression analysis indicated that cTNI and Rad-score were independent risk factors for MVO. The nomogram based on these two features achieved an area under the curve of 0.86 and 0.78 in the training and validation cohorts, respectively. Calibration curves and DCA indicated the clinical feasibility and utility of the nomogram. A CMR-based radiomics nomogram offers an effective means of predicting MVO without contrast agents and radiation, which could facilitate risk stratification of patients with STEMI after PCI for reperfusion.

Probabilistic Modeling for Image Registration Using Radial Basis Functions: Application to Cardiac Motion Estimation.

Cardiovascular diseases (CVDs) are the leading cause of death, affecting the cardiac dynamics over the cardiac cycle. Estimation of cardiac motion plays an essential role in many medical clinical tasks. This article proposes a probabilistic framework for image registration using compact support radial basis functions (CSRBFs) to estimate cardiac motion. A variational inference-based generative model with convolutional neural networks (CNNs) is proposed to learn the probabilistic coefficients of CSRBFs used in image deformation. We designed two networks to estimate the deformation coefficients of CSRBFs: the first one solves the spatial transformation using given control points, and the second one models the transformation using drifting control points. The given-point-based network estimates the probabilistic coefficients of control points. In contrast, the drifting-point-based model predicts the probabilistic coefficients and spatial distribution of control points simultaneously. To regularize these coefficients, we derive the bending energy (BE) in the variational bound by defining the covariance of coefficients. The proposed framework has been evaluated on the cardiac motion estimation and the calculation of the myocardial strain. In the experiments, 1409 slice pairs of end-diastolic (ED) and end-systolic (ES) phase in 4-D cardiac magnetic resonance (MR) images selected from three public datasets are employed to evaluate our networks. The experimental results show that our framework outperforms the state-of-the-art registration methods concerning the deformation smoothness and registration accuracy.

Partial voxel interpolation to reduce partial volume error of cardiac computed tomography ventricular volumetry in patients with congenital heart disease.

Varying degrees of partial volume error depending on the complexity of the endocardial borders are inevitable in threshold-based cardiac computed tomography (CT) ventricular volumetry. These errors can potentiallybe reduced by using a partial voxel interpolation (PVI) method, but this has not been tested for cardiac CT ventricular volumetry. To evaluate the partial volume error-reducing effects of the PVI method in cardiac CT ventricular volumetry among patients with congenital heart disease (CHD). The cardiac CT ventricular volumetry data were obtained from 55 patients (median age 12.0 years) with CHD. The ventricular and myocardial volumes, ejection fraction and ventricular mass-volume ratio were quantified and compared before and after the PVI method. The correlation between the myocardial volumes in theend-systolic and end-diastolic phases was tested. The effect of the PVI method on the classification of ventricular hypertrophy was evaluated. The indexed ventricular volumes after PVI were significantly smaller (7.4-11.5%) than those before PVI (P<0.001). In contrast, the indexed myocardial volumes were significantly larger (6.2-27.7%) after PVI (P<0.001). The ejection fractions and mass-volume ratios were significantly larger (1.6-2.2% and 19.7-42.5%, respectively) after PVI (P<0.001 and P<0.001, respectively). The indexed myocardial masses showed prominently high correlation between the end-systolic and end-diastolic phases (R, 0.961-0.990; P<0.001). The proportions of no and severe hypertrophy were significantly decreased (P<0.002) and increased (P<0.032), respectively, after the application of the PVI method. The PVI method can reduce partial volume error in cardiac CT ventricular volumetry among patients with CHD.

Morphological Changes of Anomalous Coronary Arteries From the Aorta During the Cardiac Cycle Assessed by IVUS in Resting Conditions.

Anomalous aortic origin of coronary artery (AAOCA) with intramural segment is associated with risk of sudden cardiac death, probably related to a compressive mechanism exerted by the aorta. However, the intramural compression occurrence and magnitude during the cardiac cycle remain unknown. We hypothesized that (1) in end diastole, the intramural segment is narrower, more elliptic, and has greater resistance than extramural segment; (2) the intramural segment experiences a further compression in systole; and (3) morphometry and its systolic changes vary within different lumen cross-sections of the intramural segment. Phasic changes of lumen cross-sectional coronary area, roundness (minimum/maximum lumen diameter), and hemodynamic resistance (Poiseuille law for noncircular sections) were derived from intravascular ultrasound pullbacks at rest for the ostial, distal intramural, and extramural segments. Data were obtained for 35 AAOCA (n=23 with intramural tract) after retrospective image-based gating and manual lumen segmentation. Differences between systolic and end-diastolic phases in each section, between sections of the same coronary, and between AAOCA with and without intramural tract were assessed by nonparametric statistical tests. In end diastole, both the ostial and distal intramural sections were more elliptical (P<0.001) than the reference extramural section and the correspondent sections in AAOCA without intramural segment. In systole, AAOCA with intramural segment showed a flattening at the ostium (-6.76% [10.82%]; P=0.024) and a flattening (-5.36% [16.56%]; P=0.011), a narrowing (-4.62% [11.38%]; P=0.020), and a resistance increase (15.61% [30.07%]; P=0.012) at the distal intramural section. No-intramural sections did not show morphological changes during the entire cardiac cycle. AAOCA with intramural segment has pathological segment-specific dynamic compression mainly in the systole under resting conditions. Studying AAOCA behavior with intravascular ultrasound during the cardiac cycle may help to evaluate and quantify the severity of the narrowing.

Cardiovascular magnetic resonance derived biventricular strain in healthy adolescents: reference values and comparison between myocardial tagging and feature-tracking

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): Instituto de Salud Carlos III (ISCIII) European Regional Development Fun/European Social Fund (A way to make Europe/Investing in your future). Background Strain measurements have demonstrated to be earlier and more sensitive markers of contractile dysfunction than ejection fraction (EF) alone. Myocardial tagging (MT) is considered the reference standard for CMR strain measurements, but its clinical application is limited because acquisition and analysis are laborious and time consuming. Feature-tracking (FT) CMR allows strain quantification using steady-state in free-precession (SSFP) sequence, which is obtained in routine CMR protocols, being easily applicable in clinical practice. Literature regarding reference strain values in pediatric populations is scarce and, to the best of our knowledge, there are no studies comparing FT and MT in healthy adolescents. Purpose To provide CMR strain reference values in healthy adolescents and compare FT to MT results. Methods A CMR study was performed in healthy adolescents enrolled in the "Early ImaginG Markers of unhealthy lifestyles in Adolescents" (EnIGMA) project using a 3-Tesla CMR scanner between March and October 2021. The imaging protocol included a cine SSFP to provide images for chamber size and function, including FT; and MT acquisitions using spatial modulation of magnetization (SPAMM) technique. Cine acquisitions included the entire left ventricular (LV) short axis and long-axis 4 chamber (4c)- and 2 chamber (2c)-views, while MT acquisitions included three short axis slices (basal, mid and apical) and long-axis 4c-view. Endo- and epicardial borders were manually contoured in the end-diastolic (ED) phase. A non-rigid, elastic algorithm was used for segmentation and registration for strain quantification. If tracking was suboptimal, the software only allowed adaptations of the contours on the ED image. Longitudinal (LS) and circumferential (CS) strains were obtained for the LV and right ventricle (RV). Results CMR scans were performed in 123 adolescents (64 girls, 52.0%) with a mean age of 16.0 years (standard deviation [SD]=0.4), a mean LVEF of 62.5% (SD=4.1) and a mean RVEF of 56.2% (SD=4.6). Reference percentiles (P) 3, 10, 25, 50, 90 and 97 for strain values by sex are provided in Figure 1. FT-measured LVLS and LVCS were higher in girls than in boys (-19.8 vs -17.8%, p&amp;lt;0.01; and -22.2 vs -21.0%, p&amp;lt;0.01; respectively), and these differences were confirmed with MT analysis. There were no consistent between-sex differences for RVLS and RVCS. Bland-Altman analyses showed reasonable agreement between FT and MT for LVLS, LVCS, RVLS and RVCS (Figure 2 A, B, C and D). Conclusions This study provided CMR derived reference values for left and right ventricle strain in adolescents aged 15 to 18 years. This work suggests that FT is a reasonably valid technique to measure LV and RV strain in this population. This information may be useful for diagnostic and prognostic implications in adolescent patients.

4D display of CT LV endocardial and epicardial models morphed from PET Rb-82 perfusion studies accurately quantifies segmental myocardial thickening.

MPI-derived LV wall thickening assessments for diagnostic purposes has been part of clinical guidelines for two decades. It relies on visual evaluation of tomographic slices or regional quantification displayed in 2D polar maps. 4D displays have not entered clinical usage nor have they been validated on their potential to provide equivalent information. The purpose of this work was to validate a 4D realistic display recently designed to quantitatively represent the thickening information from gated MPI into CT-morphed endocardial and epicardial moving surfaces. Forty patients who underwent 82Rb PET were selected based on LV perfusion quantification. CTA templates of heart anatomy were selected to represent the LV anatomy. Generic CT-derived LV endocardial and epicardial surfaces were modified to represent the end diastolic (ED) phase according to PET-derived ED LV dimensions and wall thickness. These CT myocardial surfaces were then morphed by means of thin plate spline (TPS) techniques, according to the gated PET slices count changes (WThPET) and LV wall motion (WMoPET). A geometric thickening (GeoTh) equivalent to LV WThPET was defined on epicardial and endocardial CT surfaces over the cardiac cycle and the two measures compared. WThPET and GeoTh correlations were performed on a case-by-case basis, by segment and by pooling all 17 segments. Pearson's correlation coefficients (PCC) were calculated to assess the equivalence of the two measures. Two cohorts of patients (normal and abnormal) were identified based on SSS. R coefficients were as follows: for all pooled segments PCCstress and PCCrest were respectively 0.91 and 0.89 (normal), and 0.9 and 0.91 (abnormal); when individual 17 segments were considered mean PCCstress = 0.92 [0.81-0.98] and mean PCCrest = 0.93 [0.83-0.98] for the abnormal perfusion group; mean PCCstress = 0.89 [0.78-0.97] and mean PCCrest = 0.89 [0.77-0.97] for the normal. When individual studies were considered, R was always > .70 with the exception of five abnormal studies. Inter-user analysis was also conducted. Our novel technique for the visualization of LV wall thickening by means of 4D CT endocardial and epicardial surface models accurately replicated 82Rb slice thickening results showing promise for its usage for diagnostic purposes.

SUCCESSIVE SUBSPACE LEARNING FOR CARDIAC DISEASE CLASSIFICATION WITH TWO-PHASE DEFORMATION FIELDS FROM CINE MRI.

Cardiac cine magnetic resonance imaging (MRI) has been used to characterize cardiovascular diseases (CVD), often providing a noninvasive phenotyping tool. While recently flourished deep learning based approaches using cine MRI yield accurate characterization results, the performance is often degraded by small training samples. In addition, many deep learning models are deemed a "black box," for which models remain largely elusive in how models yield a prediction and how reliable they are. To alleviate this, this work proposes a lightweight successive subspace learning (SSL) framework for CVD classification, based on an interpretable feedforward design, in conjunction with a cardiac atlas. Specifically, our hierarchical SSL model is based on (i) neighborhood voxel expansion, (ii) unsupervised subspace approximation, (iii) supervised regression, and (iv) multi-level feature integration. In addition, using two-phase 3D deformation fields, including end-diastolic and end-systolic phases, derived between the atlas and individual subjects as input offers objective means of assessing CVD, even with small training samples. We evaluate our framework on the ACDC2017 database, comprising one healthy group and four disease groups. Compared with 3D CNN-based approaches, our framework achieves superior classification performance with 140× fewer parameters, which supports its potential value in clinical use.

Right heart catheterization in Italian catheterization laboratories: results of the SICI-GISE national survey promoted by the GISE Young Committee

Over the past decades, the improvements in the diagnostic power and availability of non-invasive cardiac imaging techniques have led to a decline of right heart catheterization (RHC) performance. However, RHC remains the gold standard for diagnosing pulmonary hypertension and an essential tool for the evaluation of patient candidacy to heart transplantation. This survey was carried out jointly by the Young Committee of GISE, with the support of the SICI-GISE Society, and the ICOT group, with the aim of evaluating how the interventional cardiology community perform RHC. A web-based questionnaire based on 20 questions was distributed to SICI-GISE members. The survey was distributed to 1550 physicians with 174 (11%) responses. Most centers perform few procedures per year (<10 RHC/year) and a dedicated cardiologist is usually lacking. Patients were frequently admitted as ordinary hospitalization regimen and the most frequent indication for RHC was the hemodynamic assessment of pulmonary hypertension, followed by diagnostics of valvular diseases and advanced heart failure/heart transplantation. Indeed, the majority of participants (86%) are involved in transcatheter procedures for structural heart disease. The average time taken to perform the RHC was approximately 30-60 min. The femoral access (60%) was the most frequently used, usually by an echo-guided approach. Two-thirds of participants discontinued oral anticoagulant therapy before RHC. Only 27% of centers assess wedge position from an integrated analysis. Furthermore, the edge pressure is detected in the end-diastolic cardiac phase in half cases and in the end-expiratory phase in only 31%. The most commonly used method for cardiac output calculation was the indirect Fick method (58%). Guidance on the best practice for performing RHC is currently lacking. A more precise standardization of this demanding procedure is warranted.

Impact of Ventricular Geometric Characteristics on Myocardial Stiffness Assessment Using Shear-Wave Velocity in Healthy Children and Young Adults

Diastolic myocardial stiffness (MS) can serve as a key diagnostic parameter for congenital or acquired heart diseases. Using shear modulus and shear-wave velocity (SWV), shear-wave elastography (SWE) is an emerging ultrasound-based technique that can allow noninvasive assessment of MS. However, MS extrinsic parameters such as left ventricular geometric characteristics could affect shear-wave propagation. The aims of this study were to determine a range of normal values of MS using SWE in age groups of healthy children and young adults and to explore the impact of left ventricular geometric characteristics on SWE. Sixty healthy volunteers were recruited in the study and divided into 2 groups: neonates (0-1 months old, n=15) and >1month old (1month to 45years of age, n=45). SWE was performed using the Verasonics Vantage systems with a phased-array ultrasound probe. The anteroseptal basal segment was assessed in two views. SWE was electrocardiographically triggered during the end-diastolic phase. Conventional echocardiography was performed to assess ventricular function and anatomy. Results are presented as stiffness values along with mean velocity measurements and SDs. Simple and multivariate linear regression analyses were performed. For neonates, mean MS was 1.87±0.79kPa (range, 0.59-2.91kPa; mean SWV, 1.37±0.57m/sec), with high variability and no correlation with age (P=.239). For this age group, no statistically significant correlation was found between MS and any demographic or echocardiographic parameters (P>.05). For the >1month old group, a mean MS value of 1.67±0.53kPa was observed (range, 0.6-3kPa; mean SWV, 1.29±0.49m/sec) for healthy volunteers. When paired for age, no sex-related difference was observed (P=.55). In univariate linear regression analysis, age (r=0.83, P<.01), diastolic interventricular septal thickness (r=0.72, P<.01), and left ventricular end-diastolic diameter (r=0.67, P<.01) were the parameters with the highest correlation coefficients with MS. In a multiple linear regression analysis incorporating these three parameters as cofounding factors, age was the only statistically significant parameters (r=0.81, P=.02). Diastolic MS increases linearly in children and young adults. Diastolic MS correlates more robustly with age than with myocardial and left ventricular geometric characteristics. However, the geometry affects SWV, implying the need to determine well-established boundaries in future studies for the clinical application of SWE.