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

TL;DR

This paper introduces a machine learning-based DOI estimation method using gradient tree boosting for monolithic scintillator crystals in PET, achieving improved and uniform spatial resolution compared to traditional methods.

Contribution

The paper presents a novel DOI estimation approach using gradient tree boosting, optimized for FPGA implementation, with superior uniformity and accuracy over existing single observable methods.

Findings

Achieved ~2.12 mm FWHM spatial resolution with GTB

GTB models show uniform performance across crystal depth

Comparable or better resolution than traditional methods

Abstract

Monolithic crystals are examined as an alternative to segmented scintillator arrays 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) at off-center positions within a PET ring. We present a novel DOI-estimation approach based on the supervised machine learning algorithm gradient tree boosting (GTB). GTB builds predictive regression models based on sequential binary comparisons (decision trees). GTB models have been shown to be implementable in FPGA if the memory requirement fits the available resources. We propose two optimization scenarios for the best possible positioning performance: One restricting the available memory to enable a future FPGA implementation and one without any restrictions. The positioning performance of the GTB models is compared with a DOI estimation method based on a single DOI observable (SO) comparable to other methods presented in literature. For a 12 mm high monolith, we achieve an averaged spatial resolution of 2.15 mm and 2.12 mm FWHM for SO and GTB models, respectively. In contrast to SO models, GTB models show a nearly uniform positioning performance over the whole crystal depth.