Experimental Study of the Acoustic Field Generated By Proton Beams in Water

Бычков, В. В.; Demidov, V.S.; Demidova, É. V.; Ishkhanov, B. S.; Krasnov, N. K.; Luk'yashin, V. E.; Lyashuk, V. I.; Maslyanyi, V. P.

doi:10.1063/1.2956231

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…The protoacoustic determination of BP range has been actively studied for the localization of BP since there is a direct correlation between the acoustic signals and BP locations [8][9][10][11][12]. When a pulsed proton beam deposits energy in a medium, the energy dissipates in fast heat expansion and emits acoustic waves due to thermoacoustic conversion of heat to pressure.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based protoacoustic signal denoising for proton range verification

Wang

Sohn

Lei

et al. 2023

Biomed. Phys. Eng. Express

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Proton therapy is a type of radiation therapy that can provide better dose distribution compared to photon therapy by delivering most of the energy at the end of range, which is called the Bragg peak (BP). The protoacoustic technique was developed to determine the BP locations in vivo, but it requires a large dose delivery to the tissue to obtain a high number of signal averaging (NSA) to achieve a sufficient signal-to-noise ratio (SNR), which is not suitable for clinical use. A novel deep learning-based technique has been proposed to denoise acoustic signals and reduce BP range uncertainty with much lower doses. Three accelerometers were placed on the distal surface of a cylindrical polyethylene (PE) phantom to collect protoacoustic signals. In total, 512 raw signals were collected at each device. Device-specific stack autoencoder (SAE) denoising models were trained to denoise the noise-containing input signals, which were generated by averaging only 1, 2, 4, 8, 16, or 24 raw signals(low NSA signals), while the cleansignals were obtained by averaging 192 raw signals (high NSA). Both supervised and unsupervised training strategies were employed, and the evaluation of the models was based on mean squared error (MSE), SNR, and BP range uncertainty. Overall, the supervised SAEs outperformed the unsupervised SAEs in BP range verification. For the high accuracy detector, it achieved a BP range uncertainty of 0.20 ± 3.44 mm by averaging over 8 raw signals, while for the other two low accuracy detectors, they achieved the BP uncertainty of 1.44 ± 6.45 mm and -0.23 ± 4.88 mm by averaging 16 raw signals, respectively. This deep learning-based denoising method has shown promising results in enhancing the SNR of protoacoustic measurements and improving the accuracy in BP range verification. It greatly reduces the dose and time for potential clinical applications.

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Section: Introductionmentioning

confidence: 99%

Deep learning-based protoacoustic signal denoising for proton range verification

Wang

Sohn

Lei

et al. 2023

Biomed. Phys. Eng. Express

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“…Protoacoustics (Albul, et al 2001; Askaryan 1957; Bychkov, et al 2008; Hayakawa, et al 1995; Jones, et al 2016a; Jones, et al 2015; Jones, et al 2014; Sulak, et al 1979)is a proposed range verification method that measures proton range based on the time-of-flight of acoustic waves emitted due to thermoacoustic conversion of heat to pressure. Protoacoustics is advantageous because it may potentially be a real time monitor.…”

Section: Introductionmentioning

confidence: 99%

Proton range verification in homogeneous materials through acoustic measurements

et al. 2018

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Clinical proton beam quality assurance (QA) requires a simple and accurate method to measure the proton beam Bragg peak (BP) depth. Protoacoustics, the measurement of the pressure waves emitted by thermal expansion resulting from proton dose deposition, may be used to obtain the depth of the BP in a phantom by measuring the time-of-flight of the pressure wave. Rectangular and cylindrical phantoms of different materials (aluminum, lead, and polyethylene) were used for protoacoustic studies. Four different methods for analyzing the protoacoustic signals are compared. Data analysis shows that, for Methods 1 and 2, plastic phantoms have better accuracy than metallic ones because of the lower speed of sound. Method 3 does not require characterizing the speed of sound in the material, but it results in the largest error. Method 4 exhibits minimal error, less than 3 mm (with an uncertainty ⩽1.5 mm) for all the materials and geometries. Psuedospectral wave-equation simulations (k-Wave MATLAB toolbox) are used to understand the origin of acoustic reflections within the phantom. The presented simulations and experiments show that protoacoustic measurements may provide a low cost and simple QA procedure for proton beam range verification as long as the proper phantoms and calculation methods are used.

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Experimental Study of the Acoustic Field Generated By Proton Beams in Water

Cited by 2 publications

References 3 publications

Deep learning-based protoacoustic signal denoising for proton range verification

Deep learning-based protoacoustic signal denoising for proton range verification

Proton range verification in homogeneous materials through acoustic measurements

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