Decision Tree-Based Demultiplexing for Prism-PET

Abstract

Signal multiplexing is necessary to reduce a large number of readout channels in positron emission tomography (PET) scanners to minimize cost and achieve lower power consumption. However, conventional weighted average energy methods cannot localize the multiplexed events and more sophisticated approaches are necessary for accurate demultiplexing. The purpose of this paper is to propose a non-parametric decision tree model for demultiplexing signals in prismatoid PET (Prism-PET) detector module that consisted of 16 × 16 lutetium yttrium oxyorthosilicate (LYSO) scintillation crystal array coupled to 8 × 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 Center of Gravity (TCoG) methods were utilized for crystal identification based on the demultiplexed pixels. The flood histograms, 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 depth-of-interaction resolution performance while using our unique light-sharing-based multiplexed readout.