  Current clinical PET systems utilize detectors where the scintillator
typically contains single elements of 3 - 6 mm width and about 20 mm height.
While providing good TOF performance, this design limits the spatial resolution
and causes radial astigmatism as the DOI remains unknown. We propose an
alternative, aiming to combine the advantages of current detectors with the DOI
capabilities shown for monolithic concepts, based on semi-monolithic
scintillators (slabs). An array of 8 monolithic LYSO slabs of dimensions 3.9 x
32 x 19 mm3 was read out by a 64-channel photosensor containing digital SiPMs.
The position estimation in the detector's monolithic and DOI direction was
based on a calibration with a fan beam collimator and the machine learning
technique gradient tree boosting (GTB). We achieved a positioning performance
in terms of mean absolute error (MAE) of 1.44 mm and 2.12 mm for DOI
considering a wide energy window of 300 - 700 keV. The energy resolution was
determined to be 11.3%. We established both an analytical and
machine-learning-based timing calibration approach and applied them for a
first-photon trigger. The analytical timing calibration corrects for electronic
and optical time skews leading to 240 ps coincidence resolving time (CRT) for a
pair of slab-detectors. The CRT was significantly improved by utilizing GTB to
predict the time difference based on specific training data and applied on top
of the analytical calibration. We achieved 209 ps for the wide energy window
and 198 ps for a narrow selection around the photopeak (411 - 561 keV). To
maintain the detector's sensitivity, no filters were applied to the data during
processing. Overall, the semi-monolithic detector provides attractive
performance characteristics. Especially, a good CRT can be achieved while
introducing DOI capabilities to the detector, making the concept suitable for
clinical PET scanners.
