Pulsed Wave Doppler Measurements of Maximum Velocity: Dependence on Sample Volume Size.

Pulsed wave (PW) Doppler ultrasound is routinely used in the clinic to assess blood flow. Our annual Doppler quality assurance tests revealed unexpectedly large errors in measurement of maximum velocity, exceeding our tolerance (error >20%), when using certain scanners with small Doppler sample volume dimensions. The aim of this study was to assess the dependence of maximum velocity estimates on PW Doppler sample volume size. A flow phantom with known steady flow was used to acquire maximum velocity estimates (maximum velocities of 24, 39 and 85 cm/s and sample volume range of 0.3-20 mm) with a variety of transducers and scanners in clinical use (51 probes from 4 manufacturers). Selected acoustic outputs were characterized using free-field hydrophone measurements. All maximum velocity estimates were within our tolerance for sample volume sizes ≥1.5 mm, although maximum velocity estimates typically increased with decreasing sample volume size. Errors exceeding our tolerance were commonly found for one manufacturer when using smaller sample volumes, resulting in up to 75% overestimation. Although intrinsic spectral broadening based on transit time considerations may help explain our findings, the sample volume dependence raises potential clinical concerns that users should be aware of and which manufacturers should consider addressing.

A method for determining local pulse wave velocity in human ascending aorta from sequential ultrasound measurements of diameter and velocity.

BACKGROUND: Pulse wave velocity (PWV) is an indicator of arterial stiffness, and predicts cardiovascular events independently of blood pressure. Currently, PWV is commonly measured by the foot-to-foot technique thus giving a global estimate of large arterial stiffness. However, and despite its importance, methods to measure the stiffness of the ascending aorta are limited. OBJECTIVE: To introduce a method for calculating local PWV in the human ascending aorta using non-invasive ultrasound measurements of its diameter (D) and flow velocity (U). APPROACH: Ten participants (four females) were recruited from Brunel University students. Ascending aortic diameter and velocity were recorded with a GE Vivid E95 equipped with a 1.5-4.5 MHz phased array transducer using M-mode in the parasternal long axis view and pulse wave Doppler in the apical five chamber view respectively. Groups of six consecutive heartbeats were selected from each 20 s run based on the most similar cycle length resulting in three groups for D and three for U each with six waveforms. Each D waveform was paired with each U waveform to calculate PWV using ln(D)U-loop method. MAIN RESULTS: The diastolic portions of the diameters or velocities waveforms were truncated to allow the pairs to have equal length and were used to construct ln(D)U-loops. The trimmed average, excluding 10% of extreme values, resulting from the 324 loops was considered representative for each participant. Overall mean local PWV for all participants was 4.1(SD = 0.9) m s-1. SIGNIFICANCE: Local PWV can be measured non-invasively in the ascending aorta using ultrasound measurements of diameter and flow velocity This should facilitate more widespread assessment of ascending aortic stiffness in larger studies.

Automatic detection of end-diastolic and end-systolic frames in 2D echocardiography.

BACKGROUND: Correctly selecting the end-diastolic and end-systolic frames on a 2D echocardiogram is important and challenging, for both human experts and automated algorithms. Manual selection is time-consuming and subject to uncertainty, and may affect the results obtained, especially for advanced measurements such as myocardial strain. METHODS AND RESULTS: We developed and evaluated algorithms which can automatically extract global and regional cardiac velocity, and identify end-diastolic and end-systolic frames. We acquired apical four-chamber 2D echocardiographic video recordings, each at least 10 heartbeats long, acquired twice at frame rates of 52 and 79 frames/s from 19 patients, yielding 38 recordings. Five experienced echocardiographers independently marked end-systolic and end-diastolic frames for the first 10 heartbeats of each recording. The automated algorithm also did this. Using the average of time points identified by five human operators as the reference gold standard, the individual operators had a root mean square difference from that gold standard of 46.5 ms. The algorithm had a root mean square difference from the human gold standard of 40.5 ms (P<.0001). Put another way, the algorithm-identified time point was an outlier in 122/564 heartbeats (21.6%), whereas the average human operator was an outlier in 254/564 heartbeats (45%). CONCLUSION: An automated algorithm can identify the end-systolic and end-diastolic frames with performance indistinguishable from that of human experts. This saves staff time, which could therefore be invested in assessing more beats, and reduces uncertainty about the reliability of the choice of frame.

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.

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

OBJECTIVES: To determine the optimal frame rate at which reliable heart walls velocities can be assessed by speckle tracking. 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.