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An Image Registration-Based Technique for Noninvasive Vascular Elastography Publisher



Valizadeh S1, 2 ; Makkiabadi B1, 2 ; Mirbagheri A1, 2 ; Soozande M1, 2 ; Manwar R4 ; Mozaffarzadeh M2, 3 ; Nasiriavanaki M4
Authors
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Authors Affiliations
  1. 1. Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
  2. 2. Research Center for Biomedical Technologies and Robotics (RCBTR), Institute for Advanced Medical Technologies (IAMT), Tehran, Iran
  3. 3. Department of Biomedical Engineering, Tarbiat Modares University, Tehran, Iran
  4. 4. Wayne State University, Bioengineering Department, Detroit, MI, United States

Source: Progress in Biomedical Optics and Imaging - Proceedings of SPIE Published:2018


Abstract

Non-invasive vascular elastography is an emerging technique in vascular tissue imaging. During the past decades, several techniques have been suggested to estimate the tissue elasticity by measuring the displacement of the Carotid vessel wall. Cross correlation-based methods are the most prevalent approaches to measure the strain exerted in the wall vessel by the blood pressure. In the case of a low pressure, the displacement is too small to be apparent in ultrasound imaging, especially in the regions far from the center of the vessel, causing a high error of displacement measurement. On the other hand, increasing the compression leads to a relatively large displacement in the regions near the center, which reduces the performance of the cross correlation-based methods. In this study, a non-rigid image registration-based technique is proposed to measure the tissue displacement for a relatively large compression. The results show that the error of the displacement measurement obtained by the proposed method is reduced by increasing the amount of compression while the error of the cross correlationbased method rises for a relatively large compression. We also used the synthetic aperture imaging method, benefiting the directivity diagram, to improve the image quality, especially in the superficial regions. The best relative root-mean-square error (RMSE) of the proposed method and the adaptive cross correlation method were 4.5% and 6%, respectively. Consequently, the proposed algorithm outperforms the conventional method and reduces the relative RMSE by 25%. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.