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Monolithic crystals are examined as an alternative to segmented scintillatorarrays in positron emission tomography (PET). Monoliths provide good energy,timing and spatial resolution including intrinsic depth of interaction (DOI)encoding. DOI allows reducing parallax errors (radial astigmatism) atoff-center positions within a PET ring. We present a novel DOI-estimationapproach based on the supervised machine learning algorithm gradient treeboosting (GTB). GTB builds predictive regression models based on sequentialbinary comparisons (decision trees). GTB models have been shown to beimplementable in FPGA if the memory requirement fits the available resources.We propose two optimization scenarios for the best possible positioningperformance: One restricting the available memory to enable a future FPGAimplementation and one without any restrictions. The positioning performance ofthe GTB models is compared with a DOI estimation method based on a single DOIobservable (SO) comparable to other methods presented in literature. For a 12mm high monolith, we achieve an averaged spatial resolution of 2.15 mm and 2.12mm FWHM for SO and GTB models, respectively. In contrast to SO models, GTBmodels show a nearly uniform positioning performance over the whole crystaldepth.

A novel DOI Positioning Algorithm for Monolithic Scintillator Crystals in PET based on Gradient Tree Boosting