Automatic quantification of left ventricular function by medical students using ultrasound.

BACKGROUND: Automatic analyses of echocardiograms may support inexperienced users in quantifying left ventricular (LV) function. We have developed an algorithm for fully automatic measurements of mitral annular plane systolic excursion (MAPSE) and mitral annular systolic (S') and early diastolic (e') peak velocities. We aimed to study the influence of user experience of automatic measurements of these indices in echocardiographic recordings acquired by medical students and clinicians. METHODS: We included 75 consecutive patients referred for echocardiography at a university hospital. The patients underwent echocardiography by clinicians (cardiologists, cardiology residents and sonographers), who obtained manual reference measurements of MAPSE by M-mode and of S' and e' by colour tissue Doppler imaging (cTDI). Immediately after, each patient was examined by 1 of 39 medical students who were instructed in image acquisition on the day of participation. Each student acquired cTDI recordings from 1 to 4 patients. All cTDI recordings by students and clinicians were analysed for MAPSE, S' and e' using a fully automatic algorithm. The automatic measurements were compared to the manual reference measurements. RESULTS: Correct tracking of the mitral annulus was feasible in 50 (67%) and 63 (84%) of the students' and clinicians' recordings, respectively (p = 0.007). Image quality was highest in the clinicians' recordings. Mean difference ± standard deviation of the automatic measurements of the students' recordings compared to the manual reference was - 0.0 ± 2.0 mm for MAPSE, 0.3 ± 1.1 cm/s for S' and 0.6 ± 1.4 cm/s for e'. The corresponding intraclass correlation coefficients for MAPSE, S' and e' were 0.85 (good), 0.89 (good) and 0.92 (excellent), respectively. Automatic measurements from the students' and clinicians' recordings were in similar agreement with the reference when mitral annular tracking was correct. CONCLUSIONS: In case of correct tracking of the mitral annulus, the agreement with reference for the automatic measurements was overall good. Low image quality reduced feasibility. Adequate image acquisition is essential for automatic analyses of LV function indices, and thus, appropriate education of the operators is mandatory. Automatic measurements may help inexperienced users of ultrasound, but do not remove the need for dedicated education and training.

Normal ranges for automatic measurements of tissue Doppler indices of mitral annular motion by echocardiography. Data from the HUNT3 Study.

BACKGROUND: Automatic quantification of left ventricular (LV) function could enhance workflow for cardiologists and assist inexperienced clinicians who perform focused cardiac ultrasound. We have developed an algorithm for automatic measurements of the mitral annular plane systolic excursion (MAPSE) and peak velocities in systole (S') and early (e') and late (a') diastole. We aimed to establish normal reference values for the automatic measurements and to compare them with manual measurements. METHODS AND RESULTS: Healthy participants (n = 1157, 52.5% women) from the HUNT3 cross-sectional population study in Norway were included. The mean age ± standard deviation (SD) was 49 ± 14 (range: 19-89) years. The algorithm measured MAPSE, S', e', and a' from apical 4-chamber color tissue Doppler imaging (cTDI) recordings. The manual measurements were obtained by two echocardiographers, who measured MAPSE by M-mode and the velocities by cTDI. For men and women, age-specific reference values were created for groups (mean ± 1.96SD) and by linear regression (mean, 95% prediction interval). Age was negatively correlated with MAPSE, S', and e' and positively correlated with a'. There were small differences between genders. Normal reference ranges were created. The coefficients of variation between automatic and manual measurements ranged from 5.5% (S') to 11.7% (MAPSE). CONCLUSION: Normal reference values for automatic measurements of LV function indices are provided. The automatic measurements were in line with the manual measurements. Implementing automatic measurements and comparison with normal ranges in ultrasound scanners can allow for quick and precise interpretation of LV function.

Automatic Measurements of Mitral Annular Plane Systolic Excursion and Velocities to Detect Left Ventricular Dysfunction.

The purpose of the study described here was to evaluate an automatic algorithm for detection of left ventricular dysfunction, based on measurements of mitral annular motion indices from color tissue Doppler apical four-chamber recordings. Two hundred twenty-one patients, among whom 49 had systolic and 11 had diastolic dysfunction, were included. Echocardiographic evaluation by cardiologists was the reference. Twenty patients were also examined by medical students. The ability of the indices to detect systolic and diastolic dysfunction were compared in receiver operating characteristic analyses, and the agreement between automatic and reference measurements was evaluated. Mitral annular plane systolic excursion ≤10 mm detected left ventricular dysfunction with 82% specificity, 76% specificity, 56% positive predictive value and 92% negative predictive value. The automatic measurements acquired from expert recordings better agreed better with the reference than those acquired from student recordings. We conclude that automatic measurements of systolic mitral annular motion indices can be helpful in detection of left ventricular dysfunction.

Fast Simulation of Dynamic Ultrasound Images Using the GPU.

Simulated ultrasound data is a valuable tool for development and validation of quantitative image analysis methods in echocardiography. Unfortunately, simulation time can become prohibitive for phantoms consisting of a large number of point scatterers. The COLE algorithm by Gao et al. is a fast convolution-based simulator that trades simulation accuracy for improved speed. We present highly efficient parallelized CPU and GPU implementations of the COLE algorithm with an emphasis on dynamic simulations involving moving point scatterers. We argue that it is crucial to minimize the amount of data transfers from the CPU to achieve good performance on the GPU. We achieve this by storing the complete trajectories of the dynamic point scatterers as spline curves in the GPU memory. This leads to good efficiency when simulating sequences consisting of a large number of frames, such as B-mode and tissue Doppler data for a full cardiac cycle. In addition, we propose a phase-based subsample delay technique that efficiently eliminates flickering artifacts seen in B-mode sequences when COLE is used without enough temporal oversampling. To assess the performance, we used a laptop computer and a desktop computer, each equipped with a multicore Intel CPU and an NVIDIA GPU. Running the simulator on a high-end TITAN X GPU, we observed two orders of magnitude speedup compared to the parallel CPU version, three orders of magnitude speedup compared to simulation times reported by Gao et al. in their paper on COLE, and a speedup of 27000 times compared to the multithreaded version of Field II, using numbers reported in a paper by Jensen. We hope that by releasing the simulator as an open-source project we will encourage its use and further development.

Realtime Automatic Assessment of Cardiac Function in Echocardiography.

Assessment of cardiac function by echocardiography is challenging for nonexperts. In a patient with dyspnea, quantification of the mitral annular excursion (MAE) and velocities is important for the diagnosis of heart failure. The displacement of the atrioventricular (AV) plane is a good indicator of systolic left ventricular function, while the peak velocities give supplementary information about the systolic and diastolic function. By measuring these parameters automatically, a preliminary diagnosis can be given by the nonexpert. We propose an automatic algorithm to localize the mitral annular points in an apical four-chamber view and estimate the MAE, as well as the systolic, early diastolic, and late diastolic tissue peak velocities, by using a deformable ventricle model for orientation and tissue Doppler data for tracking. Automatic parameter estimates from 367 tissue Doppler recordings were compared to reference measurements by experienced cardiologists to assess the accuracy of the estimation, as well as the ability to correctly detect reduced MAE, which we defined as less than 10 mm. The dataset consisted of 200 recordings from a patient population and 167 healthy from a population study. When considering the average of the septal and lateral values, the estimation error for the MAE had a standard deviation of 2.1 mm, which was reduced to 1.9 mm when excluding recordings for which the automatic segmentation failed to locate the AV plane (41 recordings). The corresponding standard deviations for the peak velocities were around 1 cm/s. The classification of MAE was correct in 90% of the cases and had a sensitivity of 83% and a specificity of 92%. We conclude that the algorithm has good accuracy and note that the estimation error for the MAE was comparable to interobserver and methodology agreements reported in the literature.