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Monolithic crystals are considered as an alternative for complex segmented scintillator arrays in positron emission tomography systems. Monoliths provide high sensitivity, good timing, and energy resolution while being cheaper than highly segmented arrays. Furthermore, monoliths enable intrinsic depth of interaction capabilities and good spatial resolutions (SRs) mostly based on statistical calibrations. To widely translate monoliths into clinical applications, a time-efficient calibration method and a positioning algorithm implementable in system architecture such as field-programmable gate arrays (FPGAs) are required. We present a novel positioning algorithm based on gradient tree boosting (GTB) and a fast fan beam calibration requiring less than 1 h per detector block. GTB is a supervised machine learning technique building a set of sequential binary decisions (decision trees). The algorithm handles different sets of input features, their combinations and partially missing data. GTB models are strongly adaptable influencing both the positioning performance and the memory requirement of trained positioning models. For an FPGA-implementation, the memory requirement is the limiting aspect. We demonstrate a general optimization and propose two different optimization scenarios: one without compromising on positioning performance and one optimizing the positioning performance for a given memory restriction. For a 12 mm high LYSO-block, we achieve an SR better than 1.4 mm FWHM.

Gradient Tree Boosting-Based Positioning Method for Monolithic Scintillator Crystals in Positron Emission Tomography