Several automated border detection approaches for three-dimensional echocardiography have been developed in recent years, allowing quantification of a range of clinically important parameters. In this review, the background and principles of these approaches and the different classes of methods are described from a practical perspective, as well as the research trends to achieve a robust method.
Rupture of vulnerable plaques in coronary arteries is the major cause of acute coronary syndromes. Most vulnerable plaques consist of a thin fibrous cap covering an atheromous core. These plaques can be identified using intravascular ultrasound (IVUS) palpography, which measures radial strain by cross-correlating RF signals at different intraluminal pressures. Multiple strain images (i.e., partial palpograms) are averaged per heart cycle to produce a more robust compounded palpogram. However, catheter motion due to cardiac activity causes misalignment of the RF signals and thus of the partial palpograms, resulting in less valid strain estimates. To compensate for in-plane catheter rotation and translation, we devised four methods based on block matching. The global rotation block matching (GRBM) and contour mapping (CMAP) methods measure catheter rotation, and local block matching (LBM) and catheter rotation and translation (CRT) estimate displacements of local tissue regions. These methods were applied to nine in vivo pullback acquisitions, made with a 20 MHz phased-array transducer. We found that all these methods significantly increase the number of valid strain estimates in the partial and compounded palpograms (P < 0:008).The best method, LBM, attained an average increase of 17% and 15%, respectively. Implementation of this method should improve the information coming from IVUS palpography, leading to better vulnerable plaque detection.
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