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

Dhutia, Niti, Zolgharni, Massoud, Mielewczik, Michael , Negoita, Madalina, Sacchi, Stefania, ManoharanDarrel, Karikaran, Francis, Darrel and Cole, Graham (2017) Open-source, vendor-independent, automated multi-beat tissue Doppler echocardiography analysis. The International Journal of Cardiovascular Imaging, 33 (8). pp. 1135-1148. ISSN 1569-5794

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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.

Keywords:Tissue Doppler, Echocardiography, Automated, Vendor-independent measurements, Myocardial velocities
Subjects:H Engineering > H673 Bioengineering
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:30864
Deposited On:27 Feb 2018 13:44

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