TY  - JOUR
AU  - Lerendegui-Marco, J.
AU  - Balibrea-Correa, J.
AU  - Babiano-Suarez, V.
AU  - Ladarescu, I.
AU  - Domingo-Pardo, C.
PY  - 2022
DA  - 2022//
TI  - Towards machine learning aided real-time range imaging in proton therapy
T2  - Sci Rep
JO  - Scientific Reports
SP  - 2735 - 17pp
VL  - 12
IS  - 1
PB  - Nature Portfolio
AB  - 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.
SN  - 2045-2322
UR  - https://arxiv.org/abs/2201.13269
UR  - https://doi.org/10.1038/s41598-022-06126-6
DO  - 10.1038/s41598-022-06126-6
LA  - English
N1  - WOS:000757537100018
ID  - Lerendegui-Marco_etal2022
ER  -