A spatial and temporal characterisation of single proton acoustic waves in proton beam cancer therapy

Objective. Mapping of dose delivery in proton beam therapy can potentially be performed by analyzing thermoacoustic emissions measured by ultrasound arrays. Here, a method is derived and demonstrated for spatial mapping of thermoacoustic sources using numerical time reversal, simulating re-transmission of measured emissions into the medium. Approach. Spatial distributions of thermoacoustic emission sources are shown to be approximated by the analytic-signal form of the time-reversed acoustic field, evaluated at the time of the initial proton pulse. Given calibration of the array sensitivity and knowledge of tissue properties, this approach approximately reconstructs the acoustic source amplitude, equal to the product of the time derivative of the radiation dose rate, mass density, and Grüneisen parameter. This approach was implemented using two models for acoustic fields of the array elements, one modeling elements as line sources and the other as rectangular radiators. Thermoacoustic source reconstructions employed previously reported measurements of emissions from proton energy deposition in tissue-mimicking phantoms. For a phantom incorporating a bone layer, reconstructions accounted for the higher sound speed in bone. Dependence of reconstruction quality on array aperture size and signal-to-noise ratio was consistent with previous acoustic simulation studies. Main Results. Thermoacoustic source distributions were successfully reconstructed from acoustic emissions measured by a linear ultrasound array. Spatial resolution of reconstructions was significantly improved in the azimuthal (array) direction by incorporation of array element diffraction. Source localization agreed well with Monte Carlo simulations of energy deposition, and was improved by incorporating effects of inhomogeneous sound speed. Significance. The presented numerical time reversal approach reconstructs thermoacoustic sources from proton beam radiation, based on straightforward processing of acoustic emissions measured by ultrasound arrays. This approach may be useful for ranging and dosimetry of clinical proton beams, if acoustic emissions of sufficient amplitude and bandwidth can be generated by therapeutic proton sources.

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Radiation-induced acoustic signal denoising using a supervised deep learning framework for imaging and therapy monitoring

Jiang,

Wang,

et al. 2023

Phys. Med. Biol.

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Radiation-induced acoustic (RA) imaging is a promising technique for visualizing the invisible radiation energy deposition in tissues, enabling new imaging modalities and real-time therapy monitoring. However, RA imaging signal often suffers from poor signal-to-noise ratios (SNRs), thus requiring measuring hundreds or even thousands of frames for averaging to achieve satisfactory quality. This repetitive measurement increases ionizing radiation dose and degrades the temporal resolution of RA imaging, limiting its clinical utility. In this study, we developed a general deep inception convolutional neural network (GDI-CNN) to denoise RA signals to substantially reduce the number of frames needed for averaging. The network employs convolutions with multiple dilations in each inception block, allowing it to encode and decode signal features with varying temporal characteristics. This design generalizes GDI-CNN to denoise acoustic signals resulting from different radiation sources. The performance of the proposed method was evaluated using experimental data of X-ray-induced acoustic, protoacoustic, and electroacoustic signals both qualitatively and quantitatively. Results demonstrated the effectiveness of GDI-CNN: it achieved X-ray-induced acoustic image quality comparable to 750-frame-averaged results using only 10-frame-averaged measurements, reducing the imaging dose of X-ray-acoustic computed tomography (XACT) by 98.7%; it realized proton range accuracy parallel to 1500-frame-averaged results using only 20-frame-averaged measurements, improving the range verification frequency in proton therapy from 0.5Hz to 37.5Hz; it reached electroacoustic image quality comparable to 750-frame-averaged results using only a single frame signal, increasing the electric field monitoring frequency from 1 fps to 1k fps. Compared to lowpass filter-based denoising, the proposed method demonstrated considerably lower mean-squared-errors, higher peak-SNR, and higher structural similarities with respect to the corresponding high-frame-averaged measurements. The proposed deep learning-based denoising framework is a generalized method for few-frame-averaged acoustic signal denoising, which significantly improves the RA imaging’s clinical utilities for low-dose imaging and real-time therapy monitoring.

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A spatial and temporal characterisation of single proton acoustic waves in proton beam cancer therapy

Cited by 4 publications

References 32 publications

Conclusions

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Reconstruction of thermoacoustic emission sources induced by proton irradiation using numerical time reversal

Radiation-induced acoustic signal denoising using a supervised deep learning framework for imaging and therapy monitoring

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