A PET detector featuring a pseudo-monolithic crystal is being developed as a more cost-effective alternative to a full monolithic crystal PET detector. This work evaluates different methods to localize the scintillation events in quartered monolithic crystals that are optically coupled. A semi-monolithic crystal assembly was formed using four 26 26 10 mm3 LYSO crystals optically coupled together using optical adhesive, to mimic a 52 52 10 mm3 monolithic crystal detector. The crystal assembly was coupled to a 64-channel multi-anode photomultiplier tube using silicon grease. The detector was calibrated using a 34 34 scan grid. Events were first filtered and depth separated using a multi-Lorentzian fit to the collected light distribution. Next, three different techniques were explored to generate the look up tables for the event positioning. The first technique was 'standard interpolation' across the interface. The second technique was 'central extrapolation', where a bin was placed at the midpoint of the interface and events positioned within the interface region were discarded. The third technique used a 'central overlap' method where an extended region was extrapolated at each interface. Events were then positioned using least-squares minimization and maximum likelihood methods. The least-squares minimization applied to the look up table generated with the standard interpolation technique had the best full width at half maximum (FWHM) intrinsic spatial resolution and the lowest bias. However, there were discontinuities in the event positioning that would most likely lead to artifacts in the reconstructed image. The central extrapolation technique also had discontinuities and a 30% sensitivity loss near the crystal-crystal interfaces. The central overlap technique had slightly degraded performance metrics, but it still provided ∼2.1 mm intrinsic spatial resolution at the crystal-crystal interface and had a symmetric and continuously varying response function. Results using maximum likelihood positioning were similar to least-squares minimization for the central overlap data.