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Sensors 2015, 15(6), 14788-14808; doi:10.3390/s150614788

Design of a Thermoacoustic Sensor for Low Intensity Ultrasound Measurements Based on an Artificial Neural Network

Faculty of Engineering, University of Alberta, Edmonton, AB T6G 2V4, Canada
Canadian National Research Council National Institute for Nanotechnology, Edmonton, AB T6G 2M9, Canada
Author to whom correspondence should be addressed.
Academic Editor: Gerhard Lindner
Received: 12 March 2015 / Revised: 11 June 2015 / Accepted: 17 June 2015 / Published: 23 June 2015
(This article belongs to the Special Issue Acoustic Waveguide Sensors)
View Full-Text   |   Download PDF [1232 KB, uploaded 23 June 2015]   |  


In therapeutic ultrasound applications, accurate ultrasound output intensities are crucial because the physiological effects of therapeutic ultrasound are very sensitive to the intensity and duration of these applications. Although radiation force balance is a benchmark technique for measuring ultrasound intensity and power, it is costly, difficult to operate, and compromised by noise vibration. To overcome these limitations, the development of a low-cost, easy to operate, and vibration-resistant alternative device is necessary for rapid ultrasound intensity measurement. Therefore, we proposed and validated a novel two-layer thermoacoustic sensor using an artificial neural network technique to accurately measure low ultrasound intensities between 30 and 120 mW/cm2. The first layer of the sensor design is a cylindrical absorber made of plexiglass, followed by a second layer composed of polyurethane rubber with a high attenuation coefficient to absorb extra ultrasound energy. The sensor determined ultrasound intensities according to a temperature elevation induced by heat converted from incident acoustic energy. Compared with our previous one-layer sensor design, the new two-layer sensor enhanced the ultrasound absorption efficiency to provide more rapid and reliable measurements. Using a three-dimensional model in the K-wave toolbox, our simulation of the ultrasound propagation process demonstrated that the two-layer design is more efficient than the single layer design. We also integrated an artificial neural network algorithm to compensate for the large measurement offset. After obtaining multiple parameters of the sensor characteristics through calibration, the artificial neural network is built to correct temperature drifts and increase the reliability of our thermoacoustic measurements through iterative training about ten seconds. The performance of the artificial neural network method was validated through a series of experiments. Compared to our previous design, the new design reduced sensing time from 20 s to 12 s, and the sensor’s average error from 3.97 mW/cm2 to 1.31 mW/cm2 respectively. View Full-Text
Keywords: Low Intensity Pulsed Ultrasound (LIPUS); thermoacoustic sensor; ultrasound intensity measurement; artificial neural network Low Intensity Pulsed Ultrasound (LIPUS); thermoacoustic sensor; ultrasound intensity measurement; artificial neural network

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Xing, J.; Chen, J. Design of a Thermoacoustic Sensor for Low Intensity Ultrasound Measurements Based on an Artificial Neural Network. Sensors 2015, 15, 14788-14808.

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