Arterial Mechanical Motion Estimation Based on a Semi-Rigid Body Deformation Approach

Sensors

Pablo Guzman³  Ghassan Hamarneh¹  Rafael Ros² 

[1] School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada; E-Mail:;Hospital Universitario San Cecilio, Servicio de Angiología y Cirugía Vascular, 18008 Granada, Spain; E-Mail:;Department of Computer Archiecture and Technology, ETSI Informática y de Telecomunicación, CITIC-UGR, University of Granada, 18071 Granada, Spain; E-Mail:

关键词: computer vision;    ultrasound;    wall motion;    arterial stiffness;    elastography;    carotid;    motion analysis;   

DOI  :  10.3390/s140609429

来源: mdpi

【 摘 要 】

Arterial motion estimation in ultrasound (US) sequences is a hard task due to noise and discontinuities in the signal derived from US artifacts. Characterizing the mechanical properties of the artery is a promising novel imaging technique to diagnose various cardiovascular pathologies and a new way of obtaining relevant clinical information, such as determining the absence of dicrotic peak, estimating the Augmentation Index (AIx), the arterial pressure or the arterial stiffness. One of the advantages of using US imaging is the non-invasive nature of the technique unlike Intra Vascular Ultra Sound (IVUS) or angiography invasive techniques, plus the relative low cost of the US units. In this paper, we propose a semi rigid deformable method based on Soft Bodies dynamics realized by a hybrid motion approach based on cross-correlation and optical flow methods to quantify the elasticity of the artery. We evaluate and compare different techniques (for instance optical flow methods) on which our approach is based. The goal of this comparative study is to identify the best model to be used and the impact of the accuracy of these different stages in the proposed method. To this end, an exhaustive assessment has been conducted in order to decide which model is the most appropriate for registering the variation of the arterial diameter over time. Our experiments involved a total of 1620 evaluations within nine simulated sequences of 84 frames each and the estimation of four error metrics. We conclude that our proposed approach obtains approximately 2.5 times higher accuracy than conventional state-of-the-art techniques.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.