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Please use this identifier to cite or link to this item: http://hdl.handle.net/10481/47291

Title: Torsional Ultrasound Sensor Optimization for Soft Tissue Characterization
Authors: Melchor Rodríguez, Juan Manuel
Muñoz Beltrán, Rafael
Rus Carlborg, Guillermo
Issue Date: 15-Jun-2017
Abstract: Torsion mechanical waves have the capability to characterize shear stiffness moduli of soft tissue. Under this hypothesis, a computational methodology is proposed to design and optimize a piezoelectrics-based transmitter and receiver to generate and measure the response of torsional ultrasonic waves. The procedure employed is divided into two steps: (i) a finite element method (FEM) is developed to obtain a transmitted and received waveform as well as a resonance frequency of a previous geometry validated with a semi-analytical simplified model and (ii) a probabilistic optimality criteria of the design based on inverse problem from the estimation of robust probability of detection (RPOD) to maximize the detection of the pathology defined in terms of changes of shear stiffness. This study collects different options of design in two separated models, in transmission and contact, respectively. The main contribution of this work describes a framework to establish such as forward, inverse and optimization procedures to choose a set of appropriate parameters of a transducer. This methodological framework may be generalizable for other different applications.
Sponsorship: This research was supported by the Intituto de Salud Carlos III, projects DTS15-00093 (EU-FEDER), UNGRIS-CE-3664, the Ministry of Education DPI2014-51870-R and Junta de Andalucia for projects P11-CTS-8089 and GGI3000IDIB.
Publisher: MDPI
Keywords: Torsional ultrasound
Probability of detection
Soft tissue mechanics
Finite element method
Inverse problem
URI: http://hdl.handle.net/10481/47291
ISSN: 1424-8220
Citation: Melchor Rodríguez, J.M.; Muñoz Beltrán, R.; Rus Carlborg, G. Torsional Ultrasound Sensor Optimization for Soft Tissue Characterization. Sensors, 17(6): 1402 (2017). [http://hdl.handle.net/10481/47291]
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