An artificial neural network to model response of a radiotherapy beam monitoring system.
Journal:
Medical physics
Published Date:
Apr 1, 2020
Abstract
PURPOSE: The integral quality monitor (IQM) is a real-time radiotherapy beam monitoring system, which consists of a spatially sensitive large-area ion chamber, mounted at the collimator of the linear accelerator (linac), and a calculation algorithm to predict the detector signal for each beam segment. By comparing the measured and predicted signals the system validates the beam delivery. The current commercial version of IQM uses an analytic method to predict the signal, which requires a semi-empirical approach to determine and optimize various calculation parameters. The process of developing the calculation model is complex and time consuming, and moreover, the model cannot be easily generalized across various beam delivery platforms with different combinations of beam energy, beam flattening, beam shaping elements, and Linac models. Therefore, as an alternative solution, we investigated the feasibility of developing a machine learning (ML) method, using an artificial neural network (ANN), to predict the ion chamber signal. In developing an ANN, it is not necessary to explicitly account for each of the elements of beam interactions with various structures in the beam path to the ion chamber.