  We investigate five different models to reconstruct the 3D $\gamma$-ray hit
coordinates in five large \lacls monolithic crystals optically coupled to
pixelated silicon photomultipliers. These scintillators have a base surface of
50 $\times$ 50 mm$^2$ and five different thicknesses, from 10 mm to 30 mm. Four
of these models are analytical prescriptions and one is based on a
Convolutional Neural Network. Average resolutions close to 1-2mm fwhm are
obtained in the transverse crystal plane for crystal thicknesses between 10 mm
and 20 mm using analytical models. For thicker crystals average resolutions of
about 3-5~mm fwhm are obtained. Depth of interaction resolutions between 1mm
and 4 mm are achieved depending on the distance of the interaction point to the
photosensor surface. We propose a Machine Learning algorithm to correct for
linearity distortions and pin-cushion effects. The latter allows one to keep a
large field of view of about 70-80\% of the crystal surface, regardless of
crystal thickness. This work is aimed at optimizing the performance of the
so-called Total Energy Detector with Compton imaging capability (i-TED) for
time-of-flight neutron capture cross-section measurements.
