In an international collaboration with Jagiellonian University in Kraków, Poland, a data correction framework was developed for the modular J-PET scanner—a cost-effective PET imaging system that uses long plastic scintillators instead of traditional, expensive inorganic crystals. Manish Das, a PhD scholar from the J-PET team and UC Davis-based Assistant Project Scientist Reimund Bayerlein successfully demonstrated promising image quality using phantom data for the first time.

The study focused on developing key data correction techniques, including attenuation correction, random coincidence correction using a delayed time window, and scatter correction via Monte Carlo simulations. A test using the standard NEMA image quality phantom showed that these methods significantly improved image uniformity and reduced unwanted residual activity. While the J-PET system still faces challenges in sensitivity and resolution, the results mark a major step toward affordable PET scanning with long axial field of view. The team aims to extend their corrections to human imaging, potentially expanding access to advanced metabolic imaging worldwide.
 

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