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Record |
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Author |
Lerendegui-Marco, J.; Balibrea-Correa, J.; Babiano-Suarez, V.; Ladarescu, I.; Domingo-Pardo, C. |
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Title |
Towards machine learning aided real-time range imaging in proton therapy |
Type |
Journal Article |
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Year |
2022 |
Publication |
Scientific Reports |
Abbreviated Journal |
Sci Rep |
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Volume |
12 |
Issue |
1 |
Pages |
2735 - 17pp |
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Keywords |
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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. |
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Address |
[Lerendegui-Marco, Jorge; Balibrea-Correa, Javier; Babiano-Suarez, Victor; Ladarescu, Ion; Domingo-Pardo, Cesar] Univ Valencia, CSIC, Inst Fis Corpuscular, Valencia, Spain, Email: jorge.lerendegui@ific.uv.es |
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Publisher |
Nature Portfolio |
Place of Publication |
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Language  |
English |
Summary Language |
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Original Title |
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Series Title |
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Series Volume |
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Edition |
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ISSN |
2045-2322 |
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Notes |
WOS:000757537100018 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
5136 |
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