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Semi-Monolithic Meta-Scintillator Simulation Proof-of-Concept, Combining Accurate DOI and TOF

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Semi-Monolithic Meta-Scintillator Simulation Proof-of-Concept, Combining Accurate DOI and TOF

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dc.contributor.author Konstantinou, Georgios es_ES
dc.contributor.author Zhang, Lei es_ES
dc.contributor.author Bonifacio, Daniel es_ES
dc.contributor.author Latella, Riccardo es_ES
dc.contributor.author Benlloch Baviera, Jose María es_ES
dc.contributor.author González Martínez, Antonio Javier es_ES
dc.contributor.author Lecoq, Paul es_ES
dc.date.accessioned 2024-09-05T18:22:35Z
dc.date.available 2024-09-05T18:22:35Z
dc.date.issued 2024-05 es_ES
dc.identifier.issn 2469-7311 es_ES
dc.identifier.uri http://hdl.handle.net/10251/207438
dc.description.abstract [EN] In this study, we propose and examine a unique semimonolithic metascintillator (SMMS) detector design, where slow scintillators (BGO or LYSO) are split into thin slabs and read by an array of SiPM, offering depth-of-interaction (DOI) information. These are alternated with thin segmented fast scintillators (plastic EJ232 or EJ232Q), also read by single SiPMs, which provides pixel-level coincidence time resolution (CTR). The structure combines layers of slow scintillators of size 0.3 x 25.5 x (15 or 24) mm(3) with fast scintillators of size 0.1 x 3.1 x (15 or 24) mm(3). We use a Monte Carlo Gate simulation to gauge this novel semimonolithic detector's performance. We found that the time resolution of SMMS is comparable to pixelated metascintillator designs with the same materials. For example, a 15-mm deep LYSO-based SMMS yielded a CTR of 121 ps before applying timewalk correction (after correction, 107-ps CTR). The equivalent BGO-based SMMS presented a CTR of 241 ps, which is a 15% divergence from metascintillator pixel experimental findings from previous works. We also applied neural networks to the photon distributions and timestamps recorded at the SiPM array, following guidelines on semimonolithic detectors. This led to determining the DOI with less than 3-mm precision and a confidence level of 0.85 in the best case, plus more than 2 standard deviations accuracy in reconstructing energy sharing and interaction energy. In summary, neural network prediction capabilities outperform standard energy calculation methods or any analytical approach on energy sharing, thanks to the improved understanding of photon distribution. es_ES
dc.description.sponsorship This work was supported in part by the European Research Council in the framework of the ERC Advanced Grant TICAL under Grant 338953, and in part by the European Research Council (ERC) through the European Union s Horizon 2020 Research and Innovation Program (4DPET) under Grant 695536. es_ES
dc.language Inglés es_ES
dc.publisher Institute of Electrical and Electronics Engineers es_ES
dc.relation.ispartof IEEE Transactions on Radiation and Plasma Medical Sciences es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Depth of interaction es_ES
dc.subject Metascintillators es_ES
dc.subject Positron emission tomography (PET) es_ES
dc.subject Scintillators es_ES
dc.subject Semi-monolithic es_ES
dc.subject Time of flight es_ES
dc.title Semi-Monolithic Meta-Scintillator Simulation Proof-of-Concept, Combining Accurate DOI and TOF es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1109/TRPMS.2024.3368802 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/338953/EU/TICAL: 4D total absorptionTime Imaging CALorimeter/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/695536/EU/Innovative PET scanner for dynamic imaging/ es_ES
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto de Instrumentación para Imagen Molecular - Institut d'Instrumentació per a Imatge Molecular es_ES
dc.description.bibliographicCitation Konstantinou, G.; Zhang, L.; Bonifacio, D.; Latella, R.; Benlloch Baviera, JM.; González Martínez, AJ.; Lecoq, P. (2024). Semi-Monolithic Meta-Scintillator Simulation Proof-of-Concept, Combining Accurate DOI and TOF. IEEE Transactions on Radiation and Plasma Medical Sciences. 8(5):482-492. https://doi.org/10.1109/TRPMS.2024.3368802 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1109/TRPMS.2024.3368802 es_ES
dc.description.upvformatpinicio 482 es_ES
dc.description.upvformatpfin 492 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 8 es_ES
dc.description.issue 5 es_ES
dc.relation.pasarela S\522839 es_ES
dc.contributor.funder European Commission es_ES


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