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

Xing J, Chen J - Sensors (Basel) (2015)

Bottom Line: 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 Article: PubMed Central - PubMed

Affiliation: Faculty of Engineering, University of Alberta, Edmonton, AB T6G 2V4, Canada. jida1@ualberta.ca.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Comparison between the estimated data sets and the real measurement data sets.
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sensors-15-14788-f011: Comparison between the estimated data sets and the real measurement data sets.

Mentions: The performance of the trained network is further evaluated at two ambient temperatures on C that has never been trained and tested. At 20 °C, a set of C from 1 to 8.5 in 0.5 increments are given to the neural network, and estimated ultrasound intensities are generated as output. At 25 °C, a temperature that has never been trained, another set of C from 2 to 10.5 in 0.5 increments were fed into the neural network. The estimated ultrasound intensities and real measurements were compared with each other in order to evaluate the performance of the neural network. The estimated and real measured data sets at temperatures of 20 °C and 25 °C were plotted in Figure 11. As illustrated in the figure, the estimated intensities and the real measurements almost entirely overlap at both temperatures, thus verifying that the trained network not only works for trained data sets, but is also valid for untrained data sets.


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

Xing J, Chen J - Sensors (Basel) (2015)

Comparison between the estimated data sets and the real measurement data sets.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507645&req=5

sensors-15-14788-f011: Comparison between the estimated data sets and the real measurement data sets.
Mentions: The performance of the trained network is further evaluated at two ambient temperatures on C that has never been trained and tested. At 20 °C, a set of C from 1 to 8.5 in 0.5 increments are given to the neural network, and estimated ultrasound intensities are generated as output. At 25 °C, a temperature that has never been trained, another set of C from 2 to 10.5 in 0.5 increments were fed into the neural network. The estimated ultrasound intensities and real measurements were compared with each other in order to evaluate the performance of the neural network. The estimated and real measured data sets at temperatures of 20 °C and 25 °C were plotted in Figure 11. As illustrated in the figure, the estimated intensities and the real measurements almost entirely overlap at both temperatures, thus verifying that the trained network not only works for trained data sets, but is also valid for untrained data sets.

Bottom Line: 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 Article: PubMed Central - PubMed

Affiliation: Faculty of Engineering, University of Alberta, Edmonton, AB T6G 2V4, Canada. jida1@ualberta.ca.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus