“…More efficient calibration setups, for example, fan beam collimators, significantly reduce the calibration time down to hours or less, which seems more feasible for scanners with many detectors. 24,30,[33][34][35] For position estimation,a wide range of algorithms have been proposed, for example, k-nearest neighbors, 25,[36][37][38] maximum likelihood, 31,39 Voronoi diagrams, 40 or neural networks. 29,[41][42][43] In previous work, we have established a positioning method based on the supervised machine learning algorithm gradient tree boosting (GTB), enabling an easy tradeoff between positioning performance and computational requirements.…”