Abstract
Pre-treatment proton radiography and computed tomography can improve precision of proton therapy. A compact imaging setup for small-animal proton radiography, based on a miniaturized Timepix detector is presented along with results from proof-of-concept experiments. The MiniPIX detector was placed behind a µ-CT calibration phantom with 10 different tissue-equivalent inserts. The intensity of the 70 MeV proton beam was adjusted such that pixel signal clusters from individual protons on the detector could be resolved. Analysis and event filtering on various cluster properties were used to suppress unwanted events. The energy deposition of the selected clusters was converted to water-equivalent thickness (WET) of the traversed material using a conversion curve based on Monte Carlo simulations and measured clusters of protons after traversing PMMA slabs of known thickness. Despite a systematic underestimation of up to 3%, retrieved WET values are in good agreement with ground truth values from literature. The achieved spatial resolution ranges from 0.3 to 0.7 mm for phantom-detector-distances of 1 to 5 cm. Applicability to living animals is currently limited by the relatively long acquisition time of up to 20 minutes per radiography. This obstacle can however be overcome with the latest detector generation Timepix3, allowing to handle higher particle rates and thus requiring shorter irradiation times.
Dokumententyp: | Zeitschriftenartikel |
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EU Funded Grant Agreement Number: | 725539 |
EU-Projekte: | Horizon 2020 > ERC Grants > ERC Consolidator Grant > ERC Grant 725539: SIRMIO - Small Animal Ion Irradiator for Research in Molecular Image-Guided Radio-Oncology |
Publikationsform: | Submitted Version |
Keywords: | proton radiography; proton radiotherapy; small animal irradiation; particle tracking; Timepix |
Fakultät: | Physik |
Themengebiete: | 500 Naturwissenschaften und Mathematik > 530 Physik
600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit |
URN: | urn:nbn:de:bvb:19-epub-103649-6 |
Sprache: | Englisch |
Dokumenten ID: | 103649 |
Datum der Veröffentlichung auf Open Access LMU: | 28. Jun. 2023, 06:09 |
Letzte Änderungen: | 04. Jan. 2024, 11:53 |
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