@Article{Lerendegui-Marco_etal2022,
author="Lerendegui-Marco, J.
and Balibrea-Correa, J.
and Babiano-Suarez, V.
and Ladarescu, I.
and Domingo-Pardo, C.",
title="Towards machine learning aided real-time range imaging in proton therapy",
journal="Scientific Reports",
year="2022",
publisher="Nature Portfolio",
volume="12",
number="1",
pages="2735 - 17pp",
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-ray energies spanning up to 5-6 MeV, rather low gamma-ray emission yields and very intense neutron induced gamma-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-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 LaCl3 crystals, when compared to other similar systems based on LYSO, CdZnTe or LaBr3. Its high time-resolution (CRT similar to 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 LaCl3 monolithic crystals, thereby achieving a high efficiency in coincidence of 0.2{\%} for a point-like 1 MeV gamma-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) 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-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.",
optnote="WOS:000757537100018",
optnote="exported from refbase (https://references.ific.uv.es/refbase/show.php?record=5136), last updated on Mon, 07 Mar 2022 08:34:46 +0000",
issn="2045-2322",
doi="10.1038/s41598-022-06126-6",
opturl="https://arxiv.org/abs/2201.13269",
opturl="https://doi.org/10.1038/s41598-022-06126-6",
language="English"
}