Towards machine learning aided real-time range imaging in proton therapy

Abstract

Compton imaging represents a promising technique for range verification in proton therapy treatments. In this work, we report on the advantageous aspects of the i-TED detector for proton-range monitoring, based on the results of the first Monte Carlo study of its applicability to this field. i-TED is an array of Compton cameras, that have been specifically designed for neutron-capture nuclear physics experiments, which are characterized by [Formula: see text]-ray energies spanning up to 5-6 MeV, rather low [Formula: see text]-ray emission yields and very intense neutron induced [Formula: see text]-ray backgrounds. Our developments to cope with these three aspects are concomitant with those required in the field of hadron therapy, especially in terms of high efficiency for real-time monitoring, low sensitivity to neutron backgrounds and reliable performance at the high [Formula: see text]-ray energies. We find that signal-to-background ratios can be appreciably improved with i-TED thanks to its light-weight design and the low neutron-capture cross sections of its LaCl[Formula: see text] crystals, when compared to other similar systems based on LYSO, CdZnTe or LaBr[Formula: see text]. Its high time-resolution (CRT [Formula: see text] 500 ps) represents an additional advantage for background suppression when operated in pulsed HT mode. Each i-TED Compton module features two detection planes of very large LaCl[Formula: see text] monolithic crystals, thereby achieving a high efficiency in coincidence of 0.2% for a point-like 1 MeV [Formula: see text]-ray source at 5 cm distance. This leads to sufficient statistics for reliable image reconstruction with an array of four i-TED detectors assuming clinical intensities of 10[Formula: see text] protons per treatment point. The use of a two-plane design instead of three-planes has been preferred owing to the higher attainable efficiency for double time-coincidences than for threefold events. The loss of full-energy events for high energy [Formula: see text]-rays is compensated by means of machine-learning based algorithms, which allow one to enhance the signal-to-total ratio up to a factor of 2.