Dictionary-based Protoacoustic Dose Map Imaging for Proton Range Verification

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Proton radiotherapy has the potential to provide state-of-the-art dose conformality in the tumor area, reducing possible adverse effects on surrounding organs at risk. However, uncertainties in the exact location of the proton Bragg peak inside the patient prevent this technique from achieving full clinical potential. In this context, in vivo verification of the range of protons in patients is key to reduce uncertainty margins. Protoacoustic range verification employs acoustic pressure waves generated by protons due to the radio-induced thermoacoustic effect to reconstruct the dose deposited in a patient during proton therapy. In this paper, we propose to use the a priori knowledge of the shape of the proton dose distribution to create a dictionary with the expected ultrasonic signals at predetermined detector locations. Using this dictionary, the reconstruction of deposited dose is performed by matching pre-calculated dictionary acoustic signals with data acquired online during treatment. The dictionary method was evaluated on a single-field proton plan for a prostate cancer patient. Dose calculation was performed with the open-source treatment planning system matRad, while acoustic wave propagation was carried out with k-Wave. We studied the ability of the proposed dictionary method to detect range variations caused by anatomical changes in tissue density, and alterations of lateral and longitudinal beam position. Our results show that the dictionary-based protoacoustic method was able to identify the changes in range originated by all the alterations introduced, with an average accuracy of 1.4 mm. This procedure could be used for in vivo verification, comparing the measured signals with the precalculated dictionary.
Work supported by several public agencies: Spanish Government (FPA2015-65035-P, RTC-2015-3772-1, RTI2018-095800-A-I00), Comunidad de Madrid (B2017/BMD-3888 PRONTO-CM), European Regional Funds and the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 793576 (CAPPERAM).