Feasibility of using a reliable automated Doppler flow velocity measurements for research and clinical practices

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.

[1]  Arturo Evangelista,et al.  Recommendations for the evaluation of left ventricular diastolic function by echocardiography. , 2009, Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography.

[2]  Milan Sonka,et al.  Automated analysis of Doppler ultrasound velocity flow diagrams , 2001, IEEE Transactions on Medical Imaging.

[3]  Hayit Greenspan,et al.  Doppler echocardiography flow-velocity image analysis for patients with atrial fibrillation. , 2005, Ultrasound in medicine & biology.

[4]  Sergio Cerutti,et al.  Nearly automated analysis of coronary Doppler flow velocity from transthoracic ultrasound images: validation with manual tracings , 2007, Medical & Biological Engineering & Computing.

[5]  A. Hughes,et al.  When is an optimization not an optimization? Evaluation of clinical implications of information content (signal-to-noise ratio) in optimization of cardiac resynchronization therapy, and how to measure and maximize it , 2010, Heart Failure Reviews.

[6]  G. de Simone,et al.  Improved cardiovascular diagnostic accuracy by pocket size imaging device in non-cardiologic outpatients: the NaUSiCa (Naples Ultrasound Stethoscope in Cardiology) study , 2010, Cardiovascular ultrasound.

[7]  Gustavo Carneiro,et al.  A probabilistic, hierarchical, and discriminant framework for rapid and accurate detection of deformable anatomic structure , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[8]  O. Shechner,et al.  Automated method for Doppler echocardiography image analysis , 2004, 2004 23rd IEEE Convention of Electrical and Electronics Engineers in Israel.

[9]  D. Francis How to reliably deliver narrow individual-patient error bars for optimization of pacemaker AV or VV delay using a "pick-the-highest" strategy with haemodynamic measurements. , 2013, International journal of cardiology.

[10]  O. Alfieri,et al.  [Guidelines on the management of valvular heart disease (version 2012). The Joint Task Force on the Management of Valvular Heart Disease of the European Society of Cardiology (ESC) and the European Association for Cardio-Thoracic Surgery (EACTS)]. , 2013, Giornale italiano di cardiologia.

[11]  Hayit Greenspan,et al.  Shape-based similarity retrieval of Doppler images for clinical decision support , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  O. Alfieri,et al.  Guidelines on the management of valvular heart disease (version 2012). , 2012, European heart journal.

[13]  Richard S C Cobbold,et al.  Human factors as a source of error in peak Doppler velocity measurement. , 2005, Journal of vascular surgery.

[14]  Gabor Fichtinger,et al.  Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008, 11th International Conference, New York, NY, USA, September 6-10, 2008, Proceedings, Part I , 2008, International Conference on Medical Image Computing and Computer-Assisted Intervention.

[15]  J. Mayet,et al.  Choosing between velocity-time-integral ratio and peak velocity ratio for calculation of the dimensionless index (or aortic valve area) in serial follow-up of aortic stenosis. , 2013, International journal of cardiology.

[16]  P. Pibarot,et al.  Optimization of Doppler echocardiographic velocity measurements using an automatic contour detection method. , 2010, Ultrasound in medicine & biology.

[17]  Dorin Comaniciu,et al.  Automatic Mitral Valve Inflow Measurements from Doppler Echocardiography , 2008, MICCAI.

[18]  Hayit Greenspan,et al.  Image analysis of Doppler echocardiography for patients with atrial fibrillation , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).