Artificial intelligence based deconvolving on megavoltage photon beam profiles for radiotherapy applications

Objective. The aim of this work is an AI based approach to reduce the volume effect of ionization chambers used to measure high energy photon beams in radiotherapy. In particular for profile measurements, the air-filled volume leads to an inaccurate measurement of the penumbra. Approach. The AI-based approach presented in this study was trained with synthetic data intended to cover a wide range of realistic linear accelerator data. The synthetic data was created by randomly generating profiles and convolving them with the lateral response function of a Semiflex 3D ionization chamber. The neuronal network was implemented using the open source tensorflow.keras machine learning framework and a U-Net architecture. The approach was validated on three accelerator types (Varian TrueBeam, Elekta VersaHD, Siemens Artiste) at FF and FFF energies between 6 MV and 18 MV at three measurement depths. For each validation, a Semiflex 3D measurement was compared against a microDiamond measurement, and the AI processed Semiflex 3D measurement was compared against the microDiamond measurement. Main results. The AI approach was validated with dataset containing 306 profiles measured with Semiflex 3D ionization chamber and microDiamond. In 90% of the cases, the AI processed Semiflex 3D dataset agrees with the microDiamond dataset within 0.5 mm/2% gamma criterion. 77% of the AI processed Semiflex 3D measurements show a penumbra difference to the microDiamond of less than 0.5 mm, 99% of less than 1 mm. Significance. This AI approach is the first in the field of dosimetry which uses synthetic training data. Thus, the approach is able to cover a wide range of accelerators and the whole specified field size range of the ionization chamber. The application of the AI approach offers an quality improvement and time saving for measurements in the water phantom, in particular for large field sizes.