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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

Schematic of the standard thermoacoustic sensor and its setup.
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sensors-15-14788-f001: Schematic of the standard thermoacoustic sensor and its setup.

Mentions: To design a sensor for measuring Isata, adoption of the conventional beam-plotting set-up would require a complicated set-up procedure (shown in Figure 1) involving the suspension and alignment of the sensor and transducer in a water tank, with a solid cylindrical absorber in the sensor to convert the incident ultrasound energy into heat. A mechanical positioning system is required for the accurate positioning and alignment of the transducer and the sensor in the water tank.


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

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

Schematic of the standard thermoacoustic sensor and its setup.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-14788-f001: Schematic of the standard thermoacoustic sensor and its setup.
Mentions: To design a sensor for measuring Isata, adoption of the conventional beam-plotting set-up would require a complicated set-up procedure (shown in Figure 1) involving the suspension and alignment of the sensor and transducer in a water tank, with a solid cylindrical absorber in the sensor to convert the incident ultrasound energy into heat. A mechanical positioning system is required for the accurate positioning and alignment of the transducer and the sensor in the water tank.

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