Computational and experimental estimation of the calibration coefficient of an in vivo counter for 18F-FDG brain activity

Abstract

The 2-[18F]fluoro-2-deoxy-d-glucose (18F-FDG) is a widely used radiopharmaceutical in Positron Emission Tomography (PET) exams, with its demand increasing over the last decade. This has resulted in a higher number of workers exposed to the risk of internal contamination during its production and clinical use. Computer modeling has proven effective in evaluating and optimizing in vivo monitoring systems. This study utilized fourteen anthropomorphic computational phantoms, including voxelized reference adult phantoms from the International Commission on Radiation Protection (ICRP) and baby and child phantoms, to compute calibration coefficients (CC) and analyze their fluctuations. The Monte Carlo N-Particle eXtended (MCNPx) code was employed to assess the impact of head-detector distance (HDD) variations on CC values. The detector was positioned at 0 cm (HDD0), 10 cm (HDD10), 50 cm (HDD50), and 100 cm (HDD100) from the head. A calibration coefficient for in vivo monitoring of 18F-FDG in the brain was determined using four physical head phantoms: a plastic head simulator named Adult Male Head Phantom (AM_HP), a 32-week-old baby (Baby32w), a 3D-printed adult male head phantom made of Acrylonitrile Butadiene Styrene of Adult Male Reference Computational Phantom (AM_HP_ABS), and a plastic adult male head (18F_HP). A 3" × 3″ thallium-doped sodium iodine (NaI(Tl)) scintillator detector was positioned at HDD0. Computational results indicated that increasing the HDD reduced the percentage difference between the minimum and maximum CC and relative error, as expected, but decreased detection efficiency. In vivo monitoring aims to detect minimal incorporated activity, thus recommending the most efficient counting geometry (HDD0). Experimental results for HDD0 with the physical phantoms showed mean CC values of 69.3, 38.5, 55.9, and 18.6 Bq.CPS−1 for AM_HP, 18F_HP, AM_HP_ABS, and Baby32w, respectively. Differences between computational and physical phantoms highlight the influence of elemental composition and geometry. This study directly addresses the significant errors associated with single-phantom approaches by providing a practical framework of calibration coefficients for diverse anatomies, thereby improving the accuracy of workers' internal dose assessments.