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=0.87) 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.
Some of the challenges with tissue Doppler measurement include: apparent inconsistency between manufacturers, uncertainty over which part of the trace to make measurements and a lack of calibration of measurements. We develop and test tools to solve these problems in echocardiography laboratories. We designed and constructed an actuator and phantom setup to produce automatic reproducible motion, and used it to compare velocities measured using 3 echocardiographic modalities: M-mode, speckle tracking, and tissue Doppler, against a non-ultrasound, optical gold standard. In the clinical phase, 25 patients underwent M-mode, speckle tracking and tissue Doppler measurements of tissue velocities. In-vitro, the M-mode and speckle tracking velocities were concordant with optical assessment. Of the three possible tissue Doppler measurement conventions (outer, middle and inner line) only the middle line agreed with the optical assessment (discrepancy -0.20 (95% confidence interval -0.44 to 0.03)cm/s, p=0.11, outer +5.19(4.65 to 5.73)cm/s, p<0.0001, inner -6.26(-6.87 to -5.65)cm/s, p<0.0001). All 4 studied manufacturers showed a similar pattern. M-mode was therefore chosen as the in-vivo gold standard. Clinical measurements of tissue velocities by speckle tracking and the middle line of the tissue Doppler were concordant with M-mode, while the outer line significantly overestimated (+1.27(0.96 to 1.59)cm/s, p<0.0001) and the inner line underestimated (-1.81(-2.11 to -1.52)cm/s, p<0.0001). Echocardiographic velocity measurements can be calibrated by simple, inexpensive tools. We found that the middle of the tissue Doppler trace represents velocity correctly. Echocardiographers requiring velocities to match between different equipment, settings or modalities should use the middle line as the “guideline”.
Echocardiographers are often unkeen to make the considerable 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 analyse more beats, we present an automated system for Doppler
envelope quantification. It analyses long Doppler strips, spanning many heartbeats, and does not require the
electrocardiogram to isolate 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 = 6.92%) with the reference consensus expert values, and indeed performed as well as the individual
experts (R2 = 0.90 to 0.96, SD = 5.66% to 7.64%). The same performance was observed for peak-velocities; (R2 = 0.98, SD = 2.95%) and (R2 = 0.93 to 0.98, SD = 2.94% to 5.12%). This automated technology allows <10 times as many beats to be acquired and analysed compared to the conventional manual approach, with each beat maintaining its accuracy.