Monte Carlo-based scatter correction for the plastic-based modular PET scanner J-PET

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Abstract
Objective. This work presents and evaluates a Monte Carlo (MC) -based scatter correction (SC) method developed for the Jagiellonian positron emission tomography (J-PET) scanner, a modular PET system based on plastic scintillators. Approach. The algorithm employs SimSET-based simulations integrated into a time-of-flight ordered-subsets expectation maximization reconstruction framework to estimate scatter contributions. Phantom studies using the NEMA image quality (IQ) phantom and a proof-of-principle human subject scan with the J-PET scanner were analyzed. To accelerate computation, lutetium–yttrium oxyorthosilicate (LYSO) crystals were also assessed in simulations as a surrogate for the native plastic material BC-404. Main results. In phantom experiments, SC improved contrast recovery coefficients by over 20% and reduced background variability by 8.5%, without introducing significant noise. Residual activity in cold regions was also considerably reduced. Substituting LYSO in the simulations decreased runtime by nearly one order of magnitude, while maintaining deviations below 6% in IQ metrics compared to BC-404. Human subject data demonstrated qualitatively reduced residual scatter and improved organ delineation. Quantitative comparison with the commercial PET/CT scanner by General Electric HealthCare (GE) discovery MI Gen 2 showed consistent activity concentration ratios across organs, although higher noise and residual scatter between organs were observed in J-PET, which most likely originates from lower count density. Significance. The proposed MC-based SC method provides robust scatter removal for J-PET, improving quantitative performance and establishing a foundation for advanced correction and reconstruction techniques. These results bring the plastic scintillator-based J-PET scanner closer to enabling clinically relevant quantitative PET imaging.
Funding Information
  • Ministry of Science and Higher Education of the Russian Federation (IAL/SP/596235/2023)
  • Narodowe Centrum Nauki (2021/42/A/ST2/00423)
  • Excellence Initiative – Research University at Jagiellonian University (SciMat and qLife Priority Research Areas budget)
  • Infrastruktura PL-Grid (PLG/2024/017688)
  • European Commission (101199807)

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