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 $$\gamma $$ γ -ray energies spanning up to 5–6 MeV, rather low $$\gamma $$ γ -ray emission yields and very intense neutron induced $$\gamma $$ γ -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 $$\gamma $$ γ -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 $$_{3}$$ 3 crystals, when compared to other similar systems based on LYSO, CdZnTe or LaBr $$_{3}$$ 3. Its high time-resolution (CRT $$\sim $$ ∼ 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 $$_{3}$$ 3 monolithic crystals, thereby achieving a high efficiency in coincidence of 0.2% for a point-like 1 MeV $$\gamma $$ γ -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 $$^{8}$$ 8 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 $$\gamma $$ γ -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.

Abstract licence: CC BY-NC-ND

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