End-diastolic Images Research Articles

Dapagliflozin reduces epicardial adipose tissue in patients with heart failure with reduced ejection fraction and type 2 diabetes

Abstract Introduction Epicardial adipose tissue (EAT) accumulation is associated with adverse outcome in heart failure with preserved ejection fraction, however its role in HF with reduced ejection fraction (HFrEF) is less clear. The relationship between EAT thickness and their impact on patients who have diabetes and heart failure remains unclear. SGLT2 inhibitors such as dapagliflozin improve outcomes in heart failure and diabetes, however their mechanism of benefit is not well understood. The aim of our study was to assess whether dapagliflozin reduced EAT in patients with type 2 diabetes and HFrEF. Methods We performed an analysis of individuals with type 2 diabetes and HFrEF who were randomised to receive either dapagliflozin or a placebo for 12 months as part of the REFORM trial. Participants had cardiac magnetic resonance (CMR) scans at baseline and at 12 months. Epicardial fat was measured utilizing 4-chamber cine images to quantify adipose tissue between the myocardium and the visceral layer of the pericardium. This assessment was carried out using the single end-diastolic image, with measurements conducted by evaluators blinded to the randomisation status of participants. Results 47 participants completed the stud. At baseline, not only EAT thickness and BMI level (placebo: 33, dapagliflozin 32, p=0.49) but also heart rate (74.39 bpm vs. 77.61 bpm, p=0.35), systolic blood pressure (132.75 mmHg vs. 135.00 mmHg, p=0.63), diastolic blood pressure (71.39 mmHg vs. 74.04 mmHg, p=0.33) and age (67 vs. 66, p=0.79) were comparable between the two groups. 23 randomised to placebo and 24 to dapagliflozin. Baseline EAT was similar in both groups (placebo: 11.54 cm², dapagliflozin 11.74 cm², p=0.414). After 12 months of treatment dapagliflozin caused a significant reduction in EAT compared to placebo (1.21 cm² vs. 0.09 cm², p=0.007). Conclusion Dapagliflozin significantly reduced epicardial adipose tissue compared to placebo in patients with type 2 diabetes and HFrEF. The reduction in EAT may play a role in the beneficial effects of dapagliflozin in heart failure patients.4 Ch CMRBox plot

Fully Automatic Quantitative Measurement of Equilibrium Radionuclide Angiocardiography Using a Convolutional Neural Network.

The aim of this study was to generate deep learning-based regions of interest (ROIs) from equilibrium radionuclide angiography datasets for left ventricular ejection fraction (LVEF) measurement. Manually drawn ROIs (mROIs) on end-systolic and end-diastolic images were extracted from reports in a Picture Archiving and Communications System. To reduce observer variability, preprocessed ROIs (pROIs) were delineated using a 41% threshold of the maximal pixel counts of the extracted mROIs and were labeled as ground-truth. Background ROIs were automatically created using an algorithm to identify areas with minimum counts within specified probability areas around the end-systolic ROI. A 2-dimensional U-Net convolutional neural network architecture was trained to generate deep learning-based ROIs (dlROIs) from pROIs. The model's performance was evaluated using Lin's concordance correlation coefficient (CCC). Bland-Altman plots were used to assess bias and 95% limits of agreement. A total of 41,462 scans (19,309 patients) were included. Strong concordance was found between LVEF measurements from dlROIs and pROIs (CCC = 85.6%; 95% confidence interval, 85.4%-85.9%), and between LVEF measurements from dlROIs and mROIs (CCC = 86.1%; 95% confidence interval, 85.8%-86.3%). In the Bland-Altman analysis, the mean differences and 95% limits of agreement of the LVEF measurements were -0.6% and -6.6% to 5.3%, respectively, for dlROIs and pROIs, and -0.4% and -6.3% to 5.4% for dlROIs and mROIs, respectively. In 37,537 scans (91%), the absolute LVEF difference between dlROIs and mROIs was <5%. Our 2-dimensional U-Net convolutional neural network architecture showed excellent performance in generating LV ROIs from equilibrium radionuclide angiography scans. It may enhance the convenience and reproducibility of LVEF measurements.

Cardiac function in a large animal model of myocardial infarction at 7 T: deep learning based automatic segmentation increases reproducibility

Cardiac magnetic resonance (CMR) imaging allows precise non-invasive quantification of cardiac function. It requires reliable image segmentation for myocardial tissue. Clinically used software usually offers automatic approaches for this step. These are, however, designed for segmentation of human images obtained at clinical field strengths. They reach their limits when applied to preclinical data and ultrahigh field strength (such as CMR of pigs at 7 T). In our study, eleven animals (seven with myocardial infarction) underwent four CMR scans each. Short-axis cine stacks were acquired and used for functional cardiac analysis. End-systolic and end-diastolic images were labelled manually by two observers and inter- and intra-observer variability were assessed. Aiming to make the functional analysis faster and more reproducible, an established deep learning (DL) model for myocardial segmentation in humans was re-trained using our preclinical 7 T data (n = 772 images and labels). We then tested the model on n = 288 images. Excellent agreement in parameters of cardiac function was found between manual and DL segmentation: For ejection fraction (EF) we achieved a Pearson’s r of 0.95, an Intraclass correlation coefficient (ICC) of 0.97, and a Coefficient of variability (CoV) of 6.6%. Dice scores were 0.88 for the left ventricle and 0.84 for the myocardium.

ECG-free cine MRI with data-driven clustering of cardiac motion for quantification of ventricular function.

Despite the widespread use of cine MRI for evaluation of cardiac function, existing real-time methods do not easily enable quantification of ventricular function. Moreover, segmented cine MRI assumes periodicity of cardiac motion. We aim to develop a self-gated, cine MRI acquisition scheme with data-driven cluster-based binning of cardiac motion. A Cartesian golden-step balanced steady-state free precession sequence with sorted k-space ordering was designed. Image data were acquired with breath-holding. Principal component analysis and k-means clustering were used for binning of cardiac phases. Cluster compactness in the time dimension was assessed using temporal variability, and dispersion in the spatial dimension was assessed using the Caliński-Harabasz index. The proposed and the reference electrocardiogram (ECG)-gated cine methods were compared using a four-point image quality score, SNR and CNR values, and Bland-Altman analyses of ventricular function. A total of 10 subjects with sinus rhythm and 8 subjects with arrhythmias underwent cardiac MRI at 3.0 T. The temporal variability was 45.6 ms (cluster) versus 24.6 ms (ECG-based) (p< 0.001), and the Caliński-Harabasz index was 59.1 ± 9.1 (cluster) versus 22.0 ± 7.1 (ECG based) (p< 0.001). In subjects with sinus rhythm, 100% of the end-systolic and end-diastolic images from both the cluster and reference approach received the highest image quality score of 4. Relative to the reference cine images, the cluster-based multiphase (cine) image quality consistently received a one-point lower score (p< 0.05), whereas the SNR and CNR values were not significantly different (p= 0.20). In cases with arrhythmias, 97.9% of the end-systolic and end-diastolic images from the cluster approach received an image quality score of 3 or more. The mean bias values for biventricular ejection fraction and volumes derived from the cluster approach versus reference cine were negligible. ECG-free cine cardiac MRI with data-driven clustering for binning of cardiac motion is feasible and enables quantification of cardiac function.

Left Ventricular Myocardial Dysfunction Evaluation in Thalassemia Patients Using Echocardiographic Radiomic Features and Machine Learning Algorithms

Heart failure caused by iron deposits in the myocardium is the primary cause of mortality in beta-thalassemia major patients. Cardiac magnetic resonance imaging (CMRI) T2* is the primary screening technique used to detect myocardial iron overload, but inherently bears some limitations. In this study, we aimed to differentiate beta-thalassemia major patients with myocardial iron overload from those without myocardial iron overload (detected by T2*CMRI) based on radiomic features extracted from echocardiography images and machine learning (ML) in patients with normal left ventricular ejection fraction (LVEF > 55%) in echocardiography. Out of 91 cases, 44 patients with thalassemia major with normal LVEF (> 55%) and T2* ≤ 20 ms and 47 people with LVEF > 55% and T2* > 20 ms as the control group were included in the study. Radiomic features were extracted for each end-systolic (ES) and end-diastolic (ED) image. Then, three feature selection (FS) methods and six different classifiers were used. The models were evaluated using various metrics, including the area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). Maximum relevance-minimum redundancy-eXtreme gradient boosting (MRMR-XGB) (AUC = 0.73, ACC = 0.73, SPE = 0.73, SEN = 0.73), ANOVA-MLP (AUC = 0.69, ACC = 0.69, SPE = 0.56, SEN = 0.83), and recursive feature elimination-K-nearest neighbors (RFE-KNN) (AUC = 0.65, ACC = 0.65, SPE = 0.64, SEN = 0.65) were the best models in ED, ES, and ED&ES datasets. Using radiomic features extracted from echocardiographic images and ML, it is feasible to predict cardiac problems caused by iron overload.

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.

Abstract 583: Validating Noninvasive Indices Of Thoracic Aortic Dissection With Magnetic Resonance Elastography

Objective: Dissection-associated aneurysms (DAA) are the leading cause of death related to chronic Type-B aortic dissections (TBAD). Conventional imaging cannot distinguish high-risk DAAs that would benefit from early surgical intervention. We propose the feasibility of aortic stiffness as measured by magnetic resonance elastography (MRE) as a biomarker to help make this distinction. We validate MRE stiffness measures in 3D-printed PVA hydrogel phantoms with human-like dissection geometries and demonstrated the first successful application of MRE in a human thoracic aorta with a healthy volunteer. Methods: All phantoms had a 10% PVA true lumen and a 10%, 15%, 20%, or 25% false lumen. The phantoms and the thoracic aorta of a healthy volunteer were imaged in a 3T-MR scanner. A pneumatic driver created mechanical vibrations within the aorta. A cardiac-gated spin-echo echo planar imaging (SE-EPI) sequence was utilized. In vivo, end-diastolic images were chosen for analysis to minimize blood flow artifacts. Results: The MRE shear stiffness of the various PVA false lumen sides of the phantoms were 32.19, 51.48, 59.29, and 73.53 ± 2.66 kPa, respectively. Figure 1 shows the correlation (R 2 = 0.99) between MRE and rheometric stiffness measurements of the four phantoms with different PVA concentrations. Figure 2 shows MRE local frequency estimations of stiffness from the human aorta with a mean shear stiffness of 3.37±0.83kPa. Conclusion: We present a validation and feasibility study of MRE in the thoracic aorta. Future research will allow for developing predictive indices for TBAD DAA development.

Automatic identification of end-diastolic and end-systolic cardiac frames from invasive coronary angiography videos.

Automatic identification of proper image frames at the end-diastolic (ED) and end-systolic (ES) frames during the review of invasive coronary angiograms (ICA) is important to assess blood flow during a cardiac cycle, reconstruct the 3D arterial anatomy from bi-planar views, and generate the complementary fusion map with myocardial images. The current identification method primarily relies on visual interpretation, making it not only time-consuming but also less reproducible. In this paper, we propose a new method to automatically identify angiographic image frames associated with the ED and ES cardiac phases. A detection algorithm is first used to detect the key points (i.e. landmarks) of coronary arteries, and then an optical flow method is employed to track the trajectories of the selected key points. The ED and ES frames are identified based on all these trajectories. Our method was tested with 62 ICA videos from two separate medical centers. Comparing consensus interpretations by two human expert readers, excellent agreement was achieved by the proposed algorithm: the agreement rates within a one-frame range were 92.99% and 92.73% for the automatic identification of the ED and ES image frames, respectively. The proposed automated method showed great potential for being an integral part of automated ICA image analysis.

Aortic valve imaging using 18F-sodium fluoride: impact of triple motion correction

BackgroundCurrent 18F-NaF assessments of aortic valve microcalcification using 18F-NaF PET/CT are based on evaluations of end-diastolic or cardiac motion-corrected (ECG-MC) images, which are affected by both patient and respiratory motion. We aimed to test the impact of employing a triple motion correction technique (3 × MC), including cardiorespiratory and gross patient motion, on quantitative and qualitative measurements.Materials and methodsFourteen patients with aortic stenosis underwent two repeat 30-min PET aortic valve scans within (29 ± 24) days. We considered three different image reconstruction protocols; an end-diastolic reconstruction protocol (standard) utilizing 25% of the acquired data, an ECG-gated (four ECG gates) reconstruction (ECG-MC), and a triple motion-corrected (3 × MC) dataset which corrects for both cardiorespiratory and patient motion. All datasets were compared to aortic valve calcification scores (AVCS), using the Agatston method, obtained from CT scans using correlation plots. We report SUVmax values measured in the aortic valve and maximum target-to-background ratios (TBRmax) values after correcting for blood pool activity.ResultsCompared to standard and ECG-MC reconstructions, increases in both SUVmax and TBRmax were observed following 3 × MC (SUVmax: Standard = 2.8 ± 0.7, ECG-MC = 2.6 ± 0.6, and 3 × MC = 3.3 ± 0.9; TBRmax: Standard = 2.7 ± 0.7, ECG-MC = 2.5 ± 0.6, and 3 × MC = 3.3 ± 1.2, all p values ≤ 0.05). 3 × MC had improved correlations (R2 value) to the AVCS when compared to the standard methods (SUVmax: Standard = 0.10, ECG-MC = 0.10, and 3 × MC = 0.20; TBRmax: Standard = 0.20, ECG-MC = 0.28, and 3 × MC = 0.46).Conclusion3 × MC improves the correlation between the AVCS and SUVmax and TBRmax and should be considered in PET studies of aortic valves using 18F-NaF.

Deep learning-based whole-heart segmentation in 4D contrast-enhanced cardiac CT

Automatic cardiac chamber and left ventricular (LV) myocardium segmentation over the cardiac cycle significantly extends the utilization of contrast-enhanced cardiac CT, potentially enabling in-depth assessment of cardiac function. Therefore, we evaluate an automatic method for cardiac chamber and LV myocardium segmentation in 4D cardiac CT.In this study, 4D contrast-enhanced cardiac CT scans of 1509 patients selected for transcatheter aortic valve implantation with 21,605 3D images, were divided into development (N = 12) and test set (N = 1497). 3D convolutional neural networks were trained with end-systolic (ES) and end-diastolic (ED) images. Dice similarity coefficient (DSC) and average symmetric surface distance (ASSD) were computed for 3D segmentations at ES and ED in the development set via cross-validation, and for 2D segmentations in four cardiac phases for 81 test set patients. Segmentation quality in the full test set of 1497 patients was assessed visually on a three-point scale per structure based on estimated overlap with the ground truth.Automatic segmentation resulted in a mean DSC of 0.89 ± 0.10 and ASSD of 1.43 ± 1.45 mm in 12 patients in 3D, and a DSC of 0.89 ± 0.08 and ASSD of 1.86 ± 1.20 mm in 81 patients in 2D. The qualitative evaluation in the whole test set of 1497 patients showed that automatic segmentations were assigned grade 1 (clinically useful) in 98.5%, 92.2%, 83.1%, 96.3%, and 91.6% of cases for LV cavity and myocardium, right ventricle, left atrium, and right atrium.Our automatic method using convolutional neural networks performed clinically useful segmentation across the cardiac cycle in a large set of 4D cardiac CT images, potentially enabling in-depth assessment of cardiac function.

Deep learning-based cardiac cine segmentation: Transfer learning application to 7T ultrahigh-field MRI.

Artificial neural networks show promising performance in automatic segmentation of cardiac MRI. However, training requires large amounts of annotated data and generalization to different vendors, field strengths, sequence parameters, and pathologies is limited. Transfer learning addresses this challenge, but specific recommendations regarding type and amount of data required is lacking. In this study, we assess data requirements for transfer learning to experimental cardiac MRI at 7T where the segmentation task can be challenging. In addition, we provide guidelines, tools, and annotated data to enable transfer learning approaches by other researchers and clinicians. A publicly available segmentation model was used to annotate a publicly available data set. This labeled data set was subsequently used to train a neural network for segmentation of left ventricle and myocardium in cardiac cine MRI. The network is used as starting point for transfer learning to 7T cine data of healthy volunteers (n = 22; 7873 images) by updating the pre-trained weights. Structured and random data subsets of different sizes were used to systematically assess data requirements for successful transfer learning. Inconsistencies in the publically available data set were corrected, labels created, and a neural network trained. On 7T cardiac cine images the model pre-trained on public imaging data, acquired at 1.5T and 3T, achieved DICELV = 0.835 and DICEMY = 0.670. Transfer learning using 7T cine data and ImageNet weight initialization improved model performance to DICELV = 0.900 and DICEMY = 0.791. Using only end-systolic and end-diastolic images reduced training data by 90%, with no negative impact on segmentation performance (DICELV = 0.908, DICEMY = 0.805). This work demonstrates and quantifies the benefits of transfer learning for cardiac cine image segmentation. We provide practical guidelines for researchers planning transfer learning projects in cardiac MRI and make data, models, and code publicly available.

Forthcoming Issues

Fetuses with an estimated fetal weight below the 10th centile have an increased risk of adverse perinatal and long-term outcomes as well as increased rates of cardiac dysfunction, which often alters cardiac size and shape of the 4-chamber view and the individual ventricles. As a result, a simple method has emerged to screen for potential cardiac dysfunction in fetuses with estimated fetal weights <10th centile by measuring the size and shape of the 4-chamber view and the size of the ventricles.To determine the number of fetuses with an abnormal size and shape of the 4-chamber view and size of the ventricles in fetuses with an estimated fetal weight <10th centile.This was a retrospective study of 50 fetuses between 25 and 37 weeks of gestation with an estimated fetal weight <10th centile. Data from their last examination were analyzed. From an end-diastolic image of the 4-chamber view, the largest basal–apical length and transverse width were measured from their corresponding epicardial borders. This allowed the 4-chamber view area and global sphericity index (4-chamber view length/4-chamber view width) to be computed. In addition, tracing along the endocardial borders with speckle tracking software enabled measurements of the right and left ventricular chamber areas and the right ventricle/left ventricle area ratios to be computed. Doppler waveform pulsatility indices from the umbilical (umbilical artery pulsatility index) and middle cerebral arteries (middle cerebral artery pulsatility index) were analyzed, and the cerebroplacental ratio (middle cerebral artery pulsatility index/umbilical artery pulsatility index) computed. Umbilical artery pulsatility indices >90th and cerebroplacental ratios <10th centile were considered abnormal. Using data from the control fetuses, the centile for each of the cardiac measurements was categorized by whether it was <10th or >90th centile, depending upon the measurement.Of the 50 fetuses with estimated fetal weight <10th centile, 50% (n = 25) had a normal umbilical artery pulsatility index and cerebroplacental ratio. These fetuses had significantly more (P < 0.02 to <0.0001) abnormalities of the size and shape of the 4-chamber view than controls. In all, 44% had a 4-chamber view area >90th centile, 32% had a 4-chamber view global sphericity index <10th centile, 56% had a 4-chamber view width >90th centile, and 80% had 1 or more abnormalities of size and/or shape. The remaining 50% of fetuses (n = 25) had abnormalities of 1 or both for the umbilical artery pulsatility index and/or cerebroplacental ratio. These fetuses had significantly higher rates of abnormalities (P <0.05 to <0.0001) than controls for the following 4-chamber view measurements: 36% had a 4-chamber view area >90th centile; 28% had a 4-chamber view global sphericity index <10th centile; and 68% had a 4-chamber view width >90th centile. Only those fetuses with an abnormal umbilical artery pulsatility index had significant changes in ventricular size; 56% had a left ventricular area <10th centile; 28% had a right ventricular area <10th centile; 36% had right ventricular/left ventricular area ratio >90th centile. One or more of the above abnormal measurements were present in 92% of the fetuses.Higher rates of abnormalities of cardiac size and shape of the 4-chamber view were found in fetuses with an estimated fetal weight <10th centile, regardless of their umbilical artery pulsatility index and cerebroplacental ratio measurements. Those with a normal umbilical artery pulsatility index and an abnormal cerebroplacental ratio had larger and wider measurements of the 4-chamber view. In addition, the shape of the 4-chamber view was more globular or round than in controls. These fetuses may have an increased risk of perinatal complications and childhood and/or adult cardiovascular disease. Screening tools derived from the 4-chamber view, acting as surrogates for ventricular dysfunction, may identify fetuses who could benefit from further comprehensive testing and future preventive interventions.

SAUN: Stack attention U-Net for left ventricle segmentation from cardiac cine magnetic resonance imaging.

PurposeQuantification of left ventricular (LV) volume, ejection fraction and myocardial mass from multi‐slice multi‐phase cine MRI requires accurate segmentation of the LV in many images. We propose a stack attention‐based convolutional neural network (CNN) approach for fully automatic segmentation from short‐axis cine MR images.MethodsTo extract the relevant spatiotemporal image features, we introduce two kinds of stack methods, spatial stack model and temporal stack model, combining the target image with its neighboring images as the input of a CNN. A stack attention mechanism is proposed to weigh neighboring image slices in order to extract the relevant features using the target image as a guide. Based on stack attention and standard U‐Net, a novel Stack Attention U‐Net (SAUN) is proposed and trained to perform the semantic segmentation task. A loss function combining cross‐entropy and Dice is used to train SAUN. The performance of the proposed method was evaluated on an internal and a public dataset using technical metrics including Dice, Hausdorff distance (HD), and mean contour distance (MCD), as well as clinical parameters, including left ventricular ejection fraction (LVEF) and myocardial mass (LVM). In addition, the results of SAUN were compared to previously presented CNN methods, including U‐Net and SegNet.ResultsThe spatial stack attention model resulted in better segmentation results than the temporal stack model. On the internal dataset comprising of 167 post‐myocardial infarction patients and 57 healthy volunteers, our method achieved a mean Dice of 0.91, HD of 3.37 mm, and MCD of 1.08 mm. Evaluation on the publicly available ACDC dataset demonstrated good generalization performance, yielding a Dice of 0.92, HD of 9.4 mm, and MCD of 0.74 mm on end‐diastolic images, and a Dice of 0.89, HD of 7.1 mm and MCD of 1.03 mm on end‐systolic images. The Pearson correlation coefficient of LVEF and LVM between automatically and manually derived results were higher than 0.98 in both datasets.ConclusionWe developed a CNN with a stack attention mechanism to automatically segment the LV chamber and myocardium from the multi‐slice short‐axis cine MRI. The experimental results demonstrate that the proposed approach exceeds existing state‐of‐the‐art segmentation methods and verify its potential clinical applicability.

Left ventricular septal convexity in differentiating hypertrophic cardiomyopathy from hypertensive heart disease – cardiac MRI study

Abstract Background Subjects with hypertrophic cardiomyopathy (HCM) and hypertensive heart disease (HHD) have left ventricular hypertrophy (LVH). It is a common clinical problem to distinguish HCM from HHD. Septal convexity (SC) into the left ventricle is increased in subjects with hypertrophic cardiomyopathy (HCM)-causing mutations with and without LVH. Purpose Our objective was to study if SC by cardiac magnetic resonance (CMR) differentiates between HCM and HHD. Methods We measured SC in 29 subjects with hypertension and LVH (left ventricular maximal wall thickness (LVMWT) ≥13 mm), in 49 subjects with HCM (LVMWT ≥13 mm) caused by the D175N mutation in the alpha-tropomyosin (TPM1) or the Q1061X mutation in the myosin binding protein C (MYBPC3) genes, and in 20 healthy controls with no LVH. SC into the LV was measured in end-diastolic 4-chamber images as the maximal distance between LV septal endocardial border and a line connecting septal mid-wall points at the level of tricuspid valve insertion and at the level of apical right ventricular insertion on the LV. Results Subjects with HCM had significantly increased septal convexity compared to subjects with HHD both in non-indexed and in BSA-indexed measurements (10.7±4.0 mm vs 4.9±2.7 mm, P&amp;lt;0.001 and 5.5±2.1 mm/m2 vs 2.4±1.3 mm/m2, P&amp;lt;0.001). To differentiate between HCM and HHD, septal convexity cutoff value of 7.85 mm performed best with sensitivity of 77% and specificity of 90%. BSA-indexed SC cutoff value of 3.68 mm differentiated between HCM and HHD with sensitivity of 81% and specificity of 86%. Conclusions CMR derived septal convexity is easily measured and is useful in discriminating between HCM caused by sarcomere mutations versus HHD. Measurement of septal convexity Funding Acknowledgement Type of funding source: Private grant(s) and/or Sponsorship. Main funding source(s): Finnish Foundation of Cardiovascular Research, the special governmental subsidy for health sciences research of the University Hospital of Kuopio

Large-scale Quality Control of Cardiac Imaging in Population Studies: Application to UK Biobank

In large population studies such as the UK Biobank (UKBB), quality control of the acquired images by visual assessment is unfeasible. In this paper, we apply a recently developed fully-automated quality control pipeline for cardiac MR (CMR) images to the first 19,265 short-axis (SA) cine stacks from the UKBB. We present the results for the three estimated quality metrics (heart coverage, inter-slice motion and image contrast in the cardiac region) as well as their potential associations with factors including acquisition details and subject-related phenotypes. Up to 14.2% of the analysed SA stacks had sub-optimal coverage (i.e. missing basal and/or apical slices), however most of them were limited to the first year of acquisition. Up to 16% of the stacks were affected by noticeable inter-slice motion (i.e. average inter-slice misalignment greater than 3.4 mm). Inter-slice motion was positively correlated with weight and body surface area. Only 2.1% of the stacks had an average end-diastolic cardiac image contrast below 30% of the dynamic range. These findings will be highly valuable for both the scientists involved in UKBB CMR acquisition and for the ones who use the dataset for research purposes.

Size and shape of the four-chamber view of the fetal heart in fetuses with an estimated fetal weight less than the tenth centile

Fetuses with an estimated fetal weight below the 10th centile have an increased risk of adverse perinatal and long-term outcomes as well as increased rates of cardiac dysfunction, which often alters cardiac size and shape of the 4-chamber view and the individual ventricles. As a result, a simple method has emerged to screen for potential cardiac dysfunction in fetuses with estimated fetal weights <10th centile by measuring the size and shape of the 4-chamber view and the size of the ventricles. To determine the number of fetuses with an abnormal size and shape of the 4-chamber view and size of the ventricles in fetuses with an estimated fetal weight <10th centile. This was a retrospective study of 50 fetuses between 25 and 37 weeks of gestation with an estimated fetal weight <10th centile. Data from their last examination were analyzed. From an end-diastolic image of the 4-chamber view, the largest basal-apical length and transverse width were measured from their corresponding epicardial borders. This allowed the 4-chamber view area and global sphericity index (4-chamber view length/4-chamber view width) to be computed. In addition, tracing along the endocardial borders with speckle tracking software enabled measurements of the right and left ventricular chamber areas and the right ventricle/left ventricle area ratios to be computed. Doppler waveform pulsatility indices from the umbilical (umbilical artery pulsatility index) and middle cerebral arteries (middle cerebral artery pulsatility index) were analyzed, and the cerebroplacental ratio (middle cerebral artery pulsatility index/umbilical artery pulsatility index) computed. Umbilical artery pulsatility indices >90th and cerebroplacental ratios <10th centile were considered abnormal. Using data from the control fetuses, the centile for each of the cardiac measurements was categorized by whether it was <10th or >90th centile, depending upon the measurement. Of the 50 fetuses with estimated fetal weight <10th centile, 50% (n= 25) had a normal umbilical artery pulsatility index and cerebroplacental ratio. These fetuses had significantly more (P < 0.02 to <0.0001) abnormalities of the size and shape of the 4-chamber view than controls. In all, 44% had a 4-chamber view area >90th centile, 32% had a 4-chamber view global sphericity index <10th centile, 56% had a 4-chamber view width >90th centile, and 80% had 1 or more abnormalities of size and/or shape. The remaining 50% of fetuses (n= 25) had abnormalities of 1 or both for the umbilical artery pulsatility index and/or cerebroplacental ratio. These fetuses had significantly higher rates of abnormalities (P <0.05 to <0.0001) than controls for the following 4-chamber view measurements: 36% had a 4-chamber view area >90th centile; 28% had a 4-chamber view global sphericity index <10th centile; and 68% had a 4-chamber view width >90th centile. Only those fetuses with an abnormal umbilical artery pulsatility index had significant changes in ventricular size; 56% had a left ventricular area <10th centile; 28% had a right ventricular area <10th centile; 36% had right ventricular/left ventricular area ratio >90th centile. One or more of the above abnormal measurements were present in 92% of the fetuses. Higher rates of abnormalities of cardiac size and shape of the 4-chamber view were found in fetuses with an estimated fetal weight <10th centile, regardless of their umbilical artery pulsatility index and cerebroplacental ratio measurements. Those with a normal umbilical artery pulsatility index and an abnormal cerebroplacental ratio had larger and wider measurements of the 4-chamber view. In addition, the shape of the 4-chamber view was more globular or round than in controls. These fetuses may have an increased risk of perinatal complications and childhood and/or adult cardiovascular disease. Screening tools derived from the 4-chamber view, acting as surrogates for ventricular dysfunction, may identify fetuses who could benefit from further comprehensive testing and future preventive interventions.

Low-dimensional representation of cardiac motion using Barycentric Subspaces: A new group-wise paradigm for estimation, analysis, and reconstruction.

One major challenge when trying to build low-dimensional representation of the cardiac motion is its natural circular pattern during a cycle, therefore making the mean image a poor descriptor of the whole sequence. Therefore, traditional approaches for the analysis of the cardiac deformation use one specific frame of the sequence - the end-diastolic (ED) frame - as a reference to study the whole motion. Consequently, this methodology is biased by this empirical choice. Moreover, the ED image might be a poor reference when looking at large deformation for example at the end-systolic (ES) frame. In this paper, we propose a novel approach to study cardiac motion in 4D image sequences using low-dimensional subspace analysis. Instead of building subspaces relying on a mean value we use a novel type of subspaces called Barycentric Subspaces which are implicitly defined as the weighted Karcher means of k+1 reference images instead of being defined with respect to one reference image. In the first part of this article, we introduce the methodological framework and the algorithms used to manipulate images within these new subspaces: how to compute the projection of a given image on the Barycentric Subspace with its coordinates, and the opposite operation of computing an image from a set of references and coordinates. Then we show how this framework can be applied to cardiac motion problems and lead to significant improvements over the single reference method. Firstly, by computing the low-dimensional representation of two populations we show that the parameters extracted correspond to relevant cardiac motion features leading to an efficient representation and discrimination of both groups. Secondly, in motion estimation, we use the projection on this low-dimensional subspace as an additional prior on the regularization in cardiac motion tracking, efficiently reducing the error of the registration between the ED and ES by almost 30%. We also derive a symmetric and transitive formulation of the registration that can be used both for frame-to-frame and frame-to-reference registration. Finally, we look at the reconstruction of the images using our proposed low-dimensional representation and show that this multi-references method using Barycentric Subspaces performs better than traditional approaches based on a single reference.

Research on the relationship between area strain and eccentric index of atherosclerotic plaques by intravascular ultrasonic elastography

Objective To assess the relationships between area strain (AS) and eccentric index (EI) of atherosclerotic plaques as seen by intravascular ultrasonic elastography (IVUSE), and to reveal the effect of EI on the plaques stability. Methods Forty purebred New Zealand rabbits were fed with a high-cholesterol diet; the abdominal aorta endothelium was balloon-injured after 2 weeks; at the end of week 12, 2 plaques with moderate echo from each rabbit were chosen for in situ imaging, and 2 consecutive frames near the end-diastole images in situ were used to construct an IVUS elastogram. Results The eccentric plaques showed significantly greater area stain (AS) than the centripetal plaques [4.77(2.92, 8.01)% vs 3.27(2.15, 4.82)%, P=0.029] with smaller plaque area and plaque burden (P<0.05). The plaque AS was positively correlated with EI (r=0.392, P=0.003). The eccentric plaques showed significantly greater AS in the shoulder than in body [4.98(3.17, 8.48)% vs 4.64(2.51, 5.92)%, P=0.008]. Conclusions The EI is one of influential factors on plaque AS.Eccentric plaques may be more vulnerable than centripetal plaques, especially in the shoulder of eccentric plaques which have greater AS than their body. Key words: Intravascular ultrasound elastography; Atherosclerotic plaque; Eccentric index; Area strain

Erratum to: Intravascular ultrasound elastography analysis of the elastic mechanical properties of atherosclerotic plaque.

To assess the elastic mechanical properties of atherosclerotic plaque with different morphological properties by intravascular ultrasound elastography (IVUSE). 30 purebred New Zealand rabbits were fed a high-cholesterol diet; the abdominal aorta endothelium was balloon-injured after 2 weeks; at week 12, 2 plaques with moderate echo from each rabbit were chosen for in situ imaging, and 2 consecutive frames near the end-diastole images in situ were used to construct an IVUS elastogram. Shear strain (SS) and area strain (AS) were greater for eccentric than centripetal plaque (SS: 2.65(2.45)% vs. 1.79 ± 0.97%, p < 0.05; AS: 4.81(4.99)% vs. 3.23 ± 1.75%, p < 0.05) but were lower with low than high plaque burden (SS: 2.14 ± 0.37% vs. 3.40 ± 0.34%, p < 0.05; AS: 3.88 ± 0.60% vs. 5.81 ± 0.54%, p < 0.05). SS and AS were significantly greater for plaque with negative than no remodeling (SS: 3.98 ± 1.53% vs. 1.82(1.40)%, p < 0.017; AS: 6.94 ± 2.24% vs. 2.59(2.87)%, p < 0.017) and were found correlated with eccentric index and plaque burden (R2 = 0.365 and R2 = 0.359, both p < 0.05). Plaques associated with eccentricity, high plaque burden and negative remodeling showed greater strain than those with centripetalism, low plaque burden and positive remodeling. Eccentric index and plaque burden may be useful to predict the elastic stability of plaque.

Intravascular ultrasound elastography analysis of the elastic mechanical properties of atherosclerotic plaque.

To assess the elastic mechanical properties of atherosclerotic plaque with different morphological properties by intravascular ultrasound elastography (IVUSE). 30 purebred New Zealand rabbits were fed a high-cholesterol diet; the abdominal aorta endothelium was balloon-injured after 2 weeks; at week 12, 2 plaques with moderate echo from each rabbit were chosen for in situ imaging, and 2 consecutive frames near the end-diastole images in situ were used to construct an IVUS elastogram. Shear strain (SS) and area strain (AS) were greater for eccentric than centripetal plaque (SS: 2.65(2.45)% vs. 1.79 ± 0.97%, p < 0.05; AS: 4.81(4.99)% vs. 3.23 ± 1.75%, p < 0.05) but were lower with low than high plaque burden (SS: 2.14 ± 0.37% vs. 3.40 ± 0.34%, p < 0.05; AS: 3.88 ± 0.60% vs. 5.81 ± 0.54%, p < 0.05). SS and AS were significantly greater for plaque with negative than no remodeling (SS: 3.98 ± 1.53% vs. 1.82(1.40)%, p < 0.017; AS: 6.94 ± 2.24% vs. 2.59(2.87)%, p < 0.017) and were found correlated with eccentric index and plaque burden (R2 = 0.365 and R2 = 0.359, both p < 0.05). Plaques associated with eccentricity, high plaque burden and negative remodeling showed greater strain than those with centripetalism, low plaque burden and positive remodeling. Eccentric index and plaque burden may be useful to predict the elastic stability of plaque.