Doppler assessment of aortic stenosis: a 25-operator study demonstrating why reading the peak velocity is superior to velocity time integral

Aims Measurements with superior reproducibility are useful clinically and research purposes. Previous reproducibility studies of Doppler assessment of aortic stenosis (AS) have compared only a pair of observers and have not explored the mechanism by which disagreement between operators occurs. Using custom-designed software which stored operators’ traces, we investigated the reproducibility of peak and velocity time integral (VTI) measurements across a much larger group of operators and explored the mechanisms by which disagreement arose. Methods and results Twenty-five observers reviewed continuous wave (CW) aortic valve (AV) and pulsed wave (PW) left ventricular outflow tract (LVOT) Doppler traces from 20 sequential cases of AS in random order. Each operator unknowingly measured each peak velocity and VTI twice. VTI tracings were stored for comparison. Measuring the peak is much more reproducible than VTI for both PW (coefficient of variation 10.1 vs. 18.0%; P < 0.001) and CW traces (coefficient of variation 4.0 vs. 10.2%; P < 0.001). VTI is inferior because the steep early and late parts of the envelope are difficult to trace reproducibly. Dimensionless index improves reproducibility because operators tended to consistently over-read or under-read on LVOT and AV traces from the same patient (coefficient of variation 9.3 vs. 17.1%; P < 0.001). Conclusion It is far more reproducible to measure the peak of a Doppler trace than the VTI, a strategy that reduces measurement variance by approximately six-fold. Peak measurements are superior to VTI because tracing the steep slopes in the early and late part of the VTI envelope is difficult to achieve reproducibly

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

Automated multi-beat tissue Doppler echocardiography analysis using deep neural networks

Tissue Doppler imaging is an essential echocardiographic technique for the non-invasive assessment of myocardial blood velocity. Image acquisition and interpretation are performed by trained operators who visually localise landmarks representing Doppler peak velocities. Current clinical guidelines recommend averaging measurements over several heartbeats. However, this manual process is both time-consuming and disruptive to workflow. An automated system for accurate beat isolation and landmark identification would be highly desirable. A dataset of tissue Doppler images was annotated by three cardiologist experts, providing a gold standard and allowing for observer variability comparisons. Deep neural networks were trained for fully automated predictions on multiple heartbeats and tested on tissue Doppler strips of arbitrary length. Automated measurements of peak Doppler velocities show good Bland–Altman agreement (average standard deviation of 0.40 cm/s) with consensus expert values; less than the inter-observer variability (0.65 cm/s). Performance is akin to individual experts (standard deviation of 0.40 to 0.75 cm/s). Our approach allows for > 26 times as many heartbeats to be analysed, compared to a manual approach. The proposed automated models can accurately and reliably make measurements on tissue Doppler images spanning several heartbeats, with performance indistinguishable from that of human experts, but with significantly shorter processing time

Frame rate required for speckle tracking echocardiography: A quantitative clinical study with open-source, vendor-independent software

Background Assessing left ventricular function with speckle tracking is useful in patient diagnosis but requires a temporal resolution that can follow myocardial motion. In this study we investigated the effect of different frame rates on the accuracy of speckle tracking results, highlighting the temporal resolution where reliable results can be obtained. Material and methods 27 patients were scanned at two different frame rates at their resting heart rate. From all acquired loops, lower temporal resolution image sequences were generated by dropping frames, decreasing the frame rate by up to 10-fold. Results Tissue velocities were estimated by automated speckle tracking. Above 40 frames/s the peak velocity was reliably measured. When frame rate was lower, the inter-frame interval containing the instant of highest velocity also contained lower velocities, and therefore the average velocity in that interval was an underestimate of the clinically desired instantaneous maximum velocity. Conclusions The higher the frame rate, the more accurately maximum velocities are identified by speckle tracking, until the frame rate drops below 40 frames/s, beyond which there is little increase in peak velocity. We provide in an online supplement the vendor-independent software we used for automatic speckle-tracked velocity assessment to help others working in this field

Automated aortic Doppler flow tracing for reproducible research and clinical measurements

In clinical practice, echocardiographers are often unkeen to make the significant time investment to make additional multiple measurements of Doppler velocity. Main hurdle to obtaining multiple measurements is the time required to manually trace a series of Doppler traces. To make it easier to analyze more beats, we present the description of an application system for automated aortic Doppler envelope quantification, compatible with a range of hardware platforms. It analyses long Doppler strips, spanning many heartbeats, and does not require electrocardiogram to separate individual beats. We tested its measurement of velocity-time-integral and peak-velocity against the reference standard defined as the average of three experts who each made three separate measurements. The automated measurements of velocity-time-integral showed strong correspondence (R2 = 0.94) and good Bland-Altman agreement (SD = 1.39 cm) with the reference consensus expert values, and indeed performed as well as the individual experts ( R2 = 0.90 to 0.96, SD = 1.05 to 1.53 cm). The same performance was observed for peak-velocities; ( R2 = 0.98, SD = 3.07 cm/s) and ( R2 = 0.93 to 0.98, SD = 2.96 to 5.18 cm/s). This automated technology allows > 10 times as many beats to be analyzed compared to the conventional manual approach. This would make clinical and research protocols more precise for the same operator effort

Open-source, vendor-independent, automated multi-beat tissue Doppler echocardiography analysis

Current guidelines for measuring cardiac function by tissue Doppler recommend using multiple beats, but this has a time cost for human operators. We present an open-source, vendor-independent, drag-and-drop software capable of automating the measurement process. A database of ~8000 tissue Doppler beats (48 patients) from the septal and lateral annuli were analyzed by three expert echocardiographers. We developed an intensity- and gradient-based automated algorithm to measure tissue Doppler velocities. We tested its performance against manual measurements from the expert human operators. Our algorithm showed strong agreement with expert human operators. Performance was indistinguishable from a human operator: for algorithm, mean difference and SDD from the mean of human operators’ estimates 0.48 ± 1.12 cm/s (R2= 0.82); for the humans individually this was 0.43 ± 1.11 cm/s (R2= 0.84), −0.88 ± 1.12 cm/s (R2= 0.84) and 0.41 ± 1.30 cm/s (R2= 0.78). Agreement between operators and the automated algorithm was preserved when measuring at either the edge or middle of the trace. The algorithm was 10-fold quicker than manual measurements (p < 0.001). This open-source, vendor-independent, drag-and-drop software can make peak velocity measurements from pulsed wave tissue Doppler traces as accurately as human experts. This automation permits rapid, bias-resistant multi-beat analysis from spectral tissue Doppler images.European Research Council and British Heart Foundatio

Image processing elements for an operator guidance system using multimodality cardiac ultrasound to improve quantitative diagnostics

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Doppler assessment of aortic stenosis: reading the peak velocity is superior to velocity time integral

Introduction Previous studies of the reproducibility of echocardiographic assessment of aortic stenosis have compared only a pair of observers. The aim of this study was to assess reproducibility across a large group of observers and compare the reproducibility of reading the peak versus the velocity time integral. Methods 25 observers reviewed continuous wave (CW) aortic valve and pulsed wave (PW) LVOT Doppler traces from 20 sequential cases of aortic stenosis in random order. Each operator unknowingly measured the peak velocity and velocity time integral (VTI) twice for each case, with the traces stored for analysis. We undertook a mixed-model analysis of the sources of variance for peak and VTI measurements. Results Measuring the peak is more reproducible than VTI for both PW (coefficient of variation 9.6% versus 15.9%, p<0.001) and CW traces (coefficient of variation 4.0% versus 9.6%, p<0.001), as shown in Figure 1. VTI is inferior because, compared to the middle, it is difficult to reproducibly trace the steep beginning (standard deviation 3.7x and 1.8x larger for CW and PW respectively) and end (standard deviation 2.4x and 1.5x larger for CW and PW respectively). Dimensionless index reduces the coefficient of variation (19% reduction for VTI, 11% reduction for peak) partly because it cancels correlated errors: an operator who over-measures a CW trace is likely to over-measure the matching PW trace (r=0.39, p<0.001 for VTI, r=0.41, p<0.001 for peak), as shown in Figure 2. Conclusions It is more reproducible to measure the peak of a Doppler trace than the VTI, because it is difficult to trace the steep slopes at the beginning and end reproducibly. The difference is non-trivial: an average operator would be 95% confident detecting a 11.1% change in peak velocity but a much larger 27.4% change in VTI. A clinical trial of an intervention for aortic stenosis with a VTI endpoint would need to be 2.4 times larger than one with a peak velocity endpoint. Part of the benefit of dimensionless index in improving reproducibility arises because it cancels individual operators tendency to consistently over- or under-read traces

Automated Multibeat Tissue Doppler Echocardiography Analysis Using Deep Neural Networks

Tissue Doppler Imaging is an essential echocardiographic technique for the non-invasive assessment of myocardial blood velocity. Interpretation by trained experts is time-consuming and disruptive to workflow. This study presents an automated deep learning model, trained and tested on Doppler strips of arbitrary length, capable of rapid beat detection and Cartesian coordinate localisation of peak velocities with accuracy indistinguishable from human experts, but with greater speed