Next Article in Journal
Assessment from a Biopsychosocial Approach of Flight-Related Neck Pain in Fighter Pilots of Spanish Air Force. An Observational Study
Next Article in Special Issue
Intracranial Flow Volume Estimation in Patients with Internal Carotid Artery Occlusion
Previous Article in Journal
Diagnostic Accuracy of Abbreviated Bi-Parametric MRI (a-bpMRI) for Prostate Cancer Detection and Screening: A Multi-Reader Study
Previous Article in Special Issue
Imaging Challenges in the Diagnosis of Anatomical Variations of the Supra-Aortic Vessels: A Case Report and Review of Literature
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Simulation of Ultrasound RF Signals Backscattered from a 3D Model of Pulsating Artery Surrounded by Tissue

by
Monika Makūnaitė
*,
Rytis Jurkonis
,
Arūnas Lukoševičius
and
Mindaugas Baranauskas
Biomedical Engineering Institute, Kaunas University of Technology, K. Baršausko Str. 59-455, LT-51423 Kaunas, Lithuania
*
Author to whom correspondence should be addressed.
Diagnostics 2022, 12(2), 232; https://doi.org/10.3390/diagnostics12020232
Submission received: 16 November 2021 / Revised: 8 January 2022 / Accepted: 13 January 2022 / Published: 18 January 2022
(This article belongs to the Special Issue Advances in Carotid Artery Imaging)

Abstract

Arterial stiffness is an independent predictor of cardiovascular events. The motion of arterial tissues during the cardiac cycle is important as a mechanical deformation representing vessel elasticity and is related to arterial stiffness. In addition, arterial pulsation is the main source of endogenous tissue micro-motions currently being studied for tissue elastography. Methods based on artery motion detection are not applied in clinical practice these days, because they must be carefully investigated in silico and in vitro before wide usage in vivo. The purpose of this paper is to propose a dynamic 3D artery model capable of reproducing the biomechanical behavior of human blood vessels surrounded by elastic tissue for endogenous deformation elastography developments and feasibility studies. The framework is based on a 3D model of a pulsating artery surrounded by tissue and simulation of linear scanning by Field II software to generate realistic dynamic RF signals and B-mode ultrasound image sequential data. The model is defined by a spatial distribution of motions, having patient-specific slopes of radial and longitudinal motion components of the artery wall and surrounding tissues. It allows for simulating the quantified mechanical micro-motions in the volume of the model. Acceptable simulation errors calculated between modeled motion patterns and those estimated from simulated RF signals and B-scan images show that this approach is suitable for the development and validation of elastography algorithms based on motion detection.
Keywords: carotid artery; ultrasound; scatterers; motion simulation; Field II carotid artery; ultrasound; scatterers; motion simulation; Field II

Share and Cite

MDPI and ACS Style

Makūnaitė, M.; Jurkonis, R.; Lukoševičius, A.; Baranauskas, M. Simulation of Ultrasound RF Signals Backscattered from a 3D Model of Pulsating Artery Surrounded by Tissue. Diagnostics 2022, 12, 232. https://doi.org/10.3390/diagnostics12020232

AMA Style

Makūnaitė M, Jurkonis R, Lukoševičius A, Baranauskas M. Simulation of Ultrasound RF Signals Backscattered from a 3D Model of Pulsating Artery Surrounded by Tissue. Diagnostics. 2022; 12(2):232. https://doi.org/10.3390/diagnostics12020232

Chicago/Turabian Style

Makūnaitė, Monika, Rytis Jurkonis, Arūnas Lukoševičius, and Mindaugas Baranauskas. 2022. "Simulation of Ultrasound RF Signals Backscattered from a 3D Model of Pulsating Artery Surrounded by Tissue" Diagnostics 12, no. 2: 232. https://doi.org/10.3390/diagnostics12020232

APA Style

Makūnaitė, M., Jurkonis, R., Lukoševičius, A., & Baranauskas, M. (2022). Simulation of Ultrasound RF Signals Backscattered from a 3D Model of Pulsating Artery Surrounded by Tissue. Diagnostics, 12(2), 232. https://doi.org/10.3390/diagnostics12020232

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop