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SUMMARY:Graph Neural Networks and Multi-Objective Optimization for Coded-M
 ask Prompt-Gamma Imaging within the SiFi-CC Collaboration
DTSTART;VALUE=DATE-TIME:20260909T101000Z
DTEND;VALUE=DATE-TIME:20260909T104000Z
DTSTAMP;VALUE=DATE-TIME:20260922T191354Z
UID:indico-contribution-2032@events.ncbj.gov.pl
DESCRIPTION:Speakers: Philippe Clement (Doctoral Schoool of Exact and Natu
 ral Sciences\, Jagiellonian Universtity\, Kraków)\n**Background and Aims:
 ** This study introduces graph neural networks (GNNs) to improve prompt-ga
 mma imaging for a 1D coded-mask gamma camera developed by the SiFi-CC coll
 aboration for proton therapy verification. \n\n**Methods:** The coded-mask
  detector features LYSO:Ce\,Ca scintillating fibers with dual-ended readou
 ts. Coincident detector signals are aggregated into event representations 
 and subsequently into cluster representations to capture spatial\, tempora
 l\, and energy information. A graph-based learning model infers interactio
 n positions and energies from these representations\, feeding the output i
 nto a Maximum-Likelihood Expectation-Maximization (MLEM) algorithm for dep
 th profile reconstruction. Finally\, image reconstruction parameters are o
 ptimized using an NSGA-III multi-objective genetic algorithm within the Op
 tuna framework.\n\n**Results:** For a dataset of 10$^{10}$ protons\, the o
 ptimized pipeline demonstrated sub-millimeter beam range accuracy\, achiev
 ing a root-mean-square error (RMSE) below 0.6 mm. \n\n**Conclusions:** Com
 bining graph-based event reconstruction with targeted image optimization s
 ignificantly improves the resolution of coded-mask prompt-gamma imaging de
 tectors.\n\nhttps://events.ncbj.gov.pl/event/468/contributions/2032/
LOCATION:
URL:https://events.ncbj.gov.pl/event/468/contributions/2032/
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