Towards efficient Monte Carlo N-Particle simulation of a positron emission tomography (PET) via source volume definition

• Main Authors: Waeleh, Nazreen, Saripan, M. Iqbal, Masarudin, Marianie, Ahmad Saad, Fathinul Fikri, Mashohor, Syamsiah, Hashim, Suhairul
• Format: Article
• Language: English
• Published: Elsevier 2022
• Online Access: http://eprints.utem.edu.my/id/eprint/26224/2/0225101022023.PDF; http://eprints.utem.edu.my/id/eprint/26224/; https://reader.elsevier.com/reader/sd/pii/S0969804322003049?token=C427E41DF12AFA61136579E3FE51A15DE04DCBB36E40C20FDB7CDFC40856BE7869571C705165B12A7D20065C71A0CFCA&originRegion=eu-west-1&originCreation=20230207004356

Monte Carlo N-Particle (MCNP) simulation has been extensively proven in nuclear medicine imaging systems, most notably in designing and optimizing new medical imaging tools. It enables more complicated geometries and the simulation of particles passing through and interacting with materials. However, a relatively long simulation time is a drawback of Monte Carlo simulation, mainly when complex geometry exists. The current study presents an alternative variance reduction technique for a modeled positron emission tomography (PET) camera by reducing the height of the source volume definition while maintaining the geometry of the simulated model. The National Electrical Manufacturers Association (NEMA) of the International Electrotechnical Commission (IEC) PET’s phantom was used with a 1 cm diameter and 7 cm height of line source placed in the middle. The first geometry was fully filled the line source with 0.50 mCi radioactivity. In contrast, the second geometry decreased the source definition to 2.4 cm in height, covering 1 cm above and below the sub-block detector level. The source volume definition approach led to a 71% reduction in the total photons to be simulated. Results showed that the proposed variance reduction strategy could produce spatial resolution as precise as fully filled geometry and sped up the simulation time by approximately 65%. Hence, this strategy can be utilized for further PET optimizing simulation studies.