Ivo Čikeš

The shape of the aortic outflow velocity profile revisited: is there a relation between its asymmetry and ventricular function in coronary artery disease?

AIMS Myocardium contracts in the beginning of ejection causing outflow acceleration, resulting in asymmetric outflow velocity profiles peaking around one-third of ejection and declining when force development declines. This article aimed to demonstrate that decreased contractility in coronary artery disease (CAD) changes outflow timing and profile symmetry. METHODS AND RESULTS Seventy-nine patients undergoing routine full dose dobutamine stress-echo (DSE) were divided into two groups based on resting wall motion and DSE response: DSE negative (DSE(neg)) (35 of 79 patients) and positive (DSE(pos)) (44 of 79 patients) which were compared with 32 healthy volunteers. Aortic CW-Doppler traces at rest were analysed semi-automatically; time-to-peak (T(mod)), ejection-time (ET(mod)), rise-time (t(rise)), and fall-time (t(fall)) were quantified. Asymmetry (asymm) was calculated as the normalized difference of left and right half of the spectrum. Normal curves were triangular, early-peaking, whereas patients showed more rounded shapes and later peaks. T(rise) was longest in DSE(pos). T(fall) was shortest in DSE(pos), followed by controls and DSE(neg). Asymm was lowest in DSE(pos), followed by controls and DSE(neg). Abnormally symmetric profiles (asymm <0.25) were found in none of the controls, 2.9% DSE(neg), and 27.3% DSE(pos). A good correlation was found between assym and ejection fraction (EF) and T(mod)/ET(mod) and EF. Notably, an LV dynamic gradient was induced in 71.4% DSE(neg) and in 18.2% DSE(pos), associated with LV hypertrophy and supernormal (very asymmetric) traces. CONCLUSION Decreased myocardial function results in a more symmetrical outflow, while very asymmetrical traces suggest increased contractility, potentially inducing intra-cavity gradients during DSE. Therefore, including outflow symmetry as a clinical measurement provides additional information on patients with CAD.

A method for registration and model-based segmentation of Doppler ultrasound images

Morphological changes of Doppler ultrasound images are an important source of information for diagnosis of cardiovascular diseases. Quantification of these flow profiles requires segmentation of the ultrasound images. In this article, we propose a new model-based method for segmentation of (aortic outflow) velocity profiles. The method is based on a procedure for registration using a geometric transformation specifically designed for matching Doppler ultrasound profiles. After manual segmentation of a model image, the model image is temporarily registered to a new image using two manually defined points in time. Next, a non-rigid registration was carried out in the velocity direction. As a similarfity measure normalized mutual information is used, while optimization is performed by a genetic algorithm. The registration method is experimentally validated using an in-silico image phantom, and showed an accuracy of 5.4%. The model based on segmentation is evaluated in a seris of aortic outflow Doppler ultrasound images from 30 normal volunteers. Comparing the automated method to the manual delineation by an expert cardiologist the method proved accurate to 6.6%. The experimental results confirm the accuracy of the approach and shows that the method can be used for the segmentation of the clinically obtained aortic outflow velocity profiles.

Analysis of Doppler Ultrasound Outflow Profiles for the Detection of changes in Cardiac Function

Detecting changes in the contractility of the heart muscle, especially in the presence of coronary artery disease, is an important medical task. From isolated myocytes, it was suggested that chronic ischemia decreases but prolongs contraction. Additionally, severe aortic stenosis shows higher but often prolonged outflow velocities. From this, we hypothesize that there is a correlation between the morphology of the aortic outflow velocity profile and myocardial function. To test this hypothesis, we (semi-) automatically analyzed continuous wave Doppler aortic outflow traces from 112 individuals. The traces were segmented and a set of morphology features were extracted. Areas under ROC curve were used as a measure of quality of each feature for detecting changes in cardiac function related to coronary artery disease. Signal analysis has shown that the probabilistic distributions of the various features are different for normal individuals and patients with ongoing disease. This result shows that aortic outflow profiles provide information on cardiac function and that the presented signal analysis and feature extraction method might be used to provide additional diagnostic information in the clinical management of coronary artery disease.