Decision Tree-Based Demultiplexing for Prism-PET

Signal multiplexing is necessary to reduce the large number of readout channels in positron emission tomography (PET) scanners to minimize cost and achieve lower power consumption. However, the conventional weighted average energy method cannot localize the multiplexed events, and more sophisticated approaches are necessary for accurate demultiplexing. The purpose of this article is to propose a nonparametric decision tree model for demultiplexing signals in the prismatoid PET (Prism-PET) detector module that consisted of a $16\times16$ lutetium yttrium oxyorthosilicate (LYSO) scintillation crystal array coupled to $8\times $ 8 silicon photomultiplier (SiPM) pixels with 64:16 multiplexed readout. A total of 64 regression trees were trained individually to demultiplex the encoded readouts for each SiPM pixel. The center of gravity (CoG) and truncated CoG (TCoG) methods were utilized for crystal identification based on the demultiplexed pixels. The flood histogram, energy resolution, and depth of interaction (DOI) resolution were measured for comparison using with and without multiplexed readouts. In conclusion, our proposed decision tree model achieved accurate results for signal demultiplexing and thus maintained the Prism-PET detector module’s high spatial and energy resolution performance while using our unique light-sharing-based multiplexed readout.