The ischaemic constellation: an alternative to the ischaemic cascade—implications for the validation of new ischaemic tests

The ischaemic cascade is the concept that progressive myocardial oxygen supply–demand mismatch causes a consistent sequence of events, starting with metabolic alterations and followed sequentially by myocardial perfusion abnormalities, wall motion abnormalities, ECG changes, and angina. This concept would suggest that investigations that detect expressions of ischaemia earlier in the cascade should be more sensitive tests of ischaemia than those that detect expressions appearing later in the cascade. However, careful review of the studies on which the ischaemic cascade is based suggests that the ischaemic cascade concept may be less well supported by the literature than assumed. In this review we explore this, discuss an alternative method for conceptualising ischaemia, and discuss the potential implications of this new approach to clinical studies and clinical practice

An optimisation-based iterative approach for speckle tracking echocardiography

Speckle tracking is the most prominent technique used to estimate the regional movement of the heart based on echocardiograms. In this study, we propose an optimised-based block matching algorithm to perform speckle tracking iteratively. The proposed technique was evaluated using a publicly available synthetic echocardiographic dataset with known ground-truth from several major vendors and for healthy/ischaemic cases. The results were compared with the results from the classic (standard) two-dimensional block matching. The proposed method presented an average displacement error of 0.57 pixels, while classic block matching provided an average error of 1.15 pixels. When estimating the segmental/regional longitudinal strain in healthy cases, the proposed method, with an average of 0.32 ± 0.53, outperformed the classic counterpart, with an average of 3.43 ± 2.84. A similar superior performance was observed in ischaemic cases. This method does not require any additional ad hoc filtering process. Therefore, it can potentially help to reduce the variability in the strain measurements caused by various post-processing techniques applied by different implementations of the speckle tracking

Contrasting effect of different cardiothoracic operations on echocardiographic right ventricular long axis velocities, and implications for interpretation of post-operative values

AbstractBackgroundPatients undergoing coronary artery bypass grafting (CABG) experience a reduction in right ventricular long axis velocities post surgery.ObjectivesWe tested whether the phenomenon of right ventricular (RV) long axis velocity decline depends on the chest being opened fully by mid-line sternotomy, pericardial incision, or on the type of operation performed.MethodBy intraoperative transoesophageal echocardiography (TEE) we recorded serial right ventricular (RV) systolic pulse-wave tissue Doppler velocities during 6 types of elective procedure: 53 CABG surgery, 15 robotic-assisted minimally-invasive CABG (RCABG), 28 aortic valve replacement (AVR), 8 minimally-invasive aortic valve replacement (mini-AVR), 5 mediastinal mass excision, and 1 left atrial myxoma excision. Pre and post operative transthoracic echocardiography (TTE) were also conducted.ResultsSurgery without substantial opening of the pericardium did not significantly reduce RV systolic velocities (RCABG 13±1.8 versus 12.4±2.7cm/s post; mini-AVR 11.9±2.3 versus 11.1±2.3cm/s; mediastinal mass excision 13.9±3.1 versus 13.8±4cm/s). In contrast, within 5min of pericardial incision those whose surgery involved full opening of the pericardium had large reductions in RV velocities: 54±11% decline with CABG (11.3±1.9 to 5.1±1.6cm/s, p<0.0001), 54±5% with AVR (12.6±1.4 to 5.7±0.6cm/s, p<0.001) and 49% with left atrial myxoma excision (11.3 to 15.8cm/s). This persisted immediately after pericardial opening to the end of surgery (61±11%, p<0.0001; 58±7%, p<0.0001; 59% respectively).ConclusionsIt is full opening of the pericardium, and not cardiac surgery in general, which causes RV long axis decline following cardiac surgery. The impact is immediate (within 5min) and persistent

Automated Segmentation of Left Ventricle in 2D echocardiography using deep learning

Following the successful application of the U-Net to medical images, there have been different encoder-decoder models proposed as an improvement to the original U-Net for segmenting echocardiographic images. This study aims to examine the performance of the state-of-the-art proposed models claimed to have better accuracies, as well as the original U-Net model by applying them to an independent dataset of patients to segment the endocardium of the Left Ventricle in 2D automatically. The prediction outputs of the models are used to evaluate the performance of the models by comparing the automated results against the expert annotations (gold standard). Our results reveal that the original U-Net model outperforms other models by achieving an average Dice coefficient of 0.92±0.05, and Hausdorff distance of 3.97±0.82

Segmentation of Left Ventricle in 2D echocardiography using deep learning

The segmentation of Left Ventricle (LV) is currently carried out manually by the experts, and the automation of this process has proved challenging due to the presence of speckle noise and the inherently poor quality of the ultrasound images. This study aims to evaluate the performance of different state-of-the-art Convolutional Neural Network (CNN) segmentation models to segment the LV endocardium in echocardiography images automatically. Those adopted methods include U-Net, SegNet, and fully convolutional DenseNets (FC-DenseNet). The prediction outputs of the models are used to assess the performance of the CNN models by comparing the automated results against the expert annotations (as the gold standard). Results reveal that the U-Net model outperforms other models by achieving an average Dice coefficient of 0.93 ± 0.04, and Hausdorff distance of 4.52 ± 0.9

Automated speckle tracking algorithm to aid on-axis imaging in echocardiography

Obtaining a “correct” view in echocardiography is a subjective process in which an operator attempts to obtain images conforming to consensus standard views. Real-time objective quantification of image alignment may assist less experienced operators, but no reliable index yet exists. We present a fully automated algorithm for detecting incorrect medial/lateral translation of an ultrasound probe by image analysis. The ability of the algorithm to distinguish optimal from sub-optimal four-chamber images was compared to that of specialists—the current “gold-standard.” The orientation assessments produced by the automated algorithm correlated well with consensus visual assessments of the specialists (r=0.87r=0.87) and compared favourably with the correlation between individual specialists and the consensus, 0.82±0.09. Each individual specialist’s assessments were within the consensus of other specialists, 75±14% of the time, and the algorithm’s assessments were within the consensus of specialists 85% of the time. The mean discrepancy in probe translation values between individual specialists and their consensus was 0.97±0.87  cm, and between the automated algorithm and specialists’ consensus was 0.92±0.70  cm. This technology could be incorporated into hardware to provide real-time guidance for image optimisation—a potentially valuable tool both for training and quality control

A systematic approach to designing reliable VV optimization methodology: Assessment of internal validity of echocardiographic, electrocardiographic and haemodynamic optimization of cardiac resynchronization therapy

AbstractBackgroundIn atrial fibrillation (AF), VV optimization of biventricular pacemakers can be examined in isolation. We used this approach to evaluate internal validity of three VV optimization methods by three criteria.Methods and resultsTwenty patients (16 men, age 75±7) in AF were optimized, at two paced heart rates, by LVOT VTI (flow), non-invasive arterial pressure, and ECG (minimizing QRS duration). Each optimization method was evaluated for: singularity (unique peak of function), reproducibility of optimum, and biological plausibility of the distribution of optima.The reproducibility (standard deviation of the difference, SDD) of the optimal VV delay was 10ms for pressure, versus 8ms (p=ns) for QRS and 34ms (p<0.01) for flow.Singularity of optimum was 85% for pressure, 63% for ECG and 45% for flow (Chi2=10.9, p<0.005).The distribution of pressure optima was biologically plausible, with 80% LV pre-excited (p=0.007). The distributions of ECG (55% LV pre-excitation) and flow (45% LV pre-excitation) optima were no different to random (p=ns).The pressure-derived optimal VV delay is unaffected by the paced rate: SDD between slow and fast heart rate is 9ms, no different from the reproducibility SDD at both heart rates.ConclusionsUsing non-invasive arterial pressure, VV delay optimization by parabolic fitting is achievable with good precision, satisfying all 3 criteria of internal validity. VV optimum is unaffected by heart rate. Neither QRS minimization nor LVOT VTI satisfy all validity criteria, and therefore seem weaker candidate modalities for VV optimization. AF, unlinking interventricular from atrioventricular delay, uniquely exposes resynchronization concepts to experimental scrutiny

Multibeat echocardiographic phase detection using deep neural networks

Background Accurate identification of end-diastolic and end-systolic frames in echocardiographic cine loops is important, yet challenging, for human experts. Manual frame selection is subject to uncertainty, affecting crucial clinical measurements, such as myocardial strain. Therefore, the ability to automatically detect frames of interest is highly desirable. Methods We have developed deep neural networks, trained and tested on multi-centre patient data, for the accurate identification of end-diastolic and end-systolic frames in apical four-chamber 2D multibeat cine loop recordings of arbitrary length. Seven experienced cardiologist experts independently labelled the frames of interest, thereby providing infallible annotations, allowing for observer variability measurements. Results When compared with the ground-truth, our model shows an average frame difference of −0.09 ± 1.10 and 0.11 ± 1.29 frames for end-diastolic and end-systolic frames, respectively. When applied to patient datasets from a different clinical site, to which the model was blind during its development, average frame differences of −1.34 ± 3.27 and −0.31 ± 3.37 frames were obtained for both frames of interest. All detection errors fall within the range of inter-observer variability: [-0.87, −5.51]±[2.29, 4.26] and [-0.97, −3.46]±[3.67, 4.68] for ED and ES events, respectively. Conclusions The proposed automated model can identify multiple end-systolic and end-diastolic frames in echocardiographic videos of arbitrary length with performance indistinguishable from that of human experts, but with significantly shorter processing time