Multi-Input Interaction Positioning and Signal Demultiplexing With Deep Learning in Semi-Monolithic Detectors

Deep learning is increasingly transforming medical imaging by enabling more accurate data interpretation and reconstruction from complex detector signals. In positron emission tomography (PET), accurate localization of photon interactions within detectors is crucial for improving image resolution and diagnostic value. This work focuses on semi-monolithic scintillation detectors, which balance pixelated and monolithic designs, and applies deep learning methods that exploit scintillation light patterns across photosensors to improve interaction positioning. We evaluated how different neural network architectures and combinations of input signals affect positioning accuracy using experimental data from two types of detector arrays: 1) a $1 8$ module used in the IMAS total-body scanner and 2) a $1 16$ module from a brain-dedicated PET, both coupled to 64-channel photosensor matrices. We compared multilayer perceptron and convolutional neural network, using either reduced 16-channel inputs (obtained via row and column summation) or the full 64-channel configuration, along with energy, time, and engineered features. Results show that deeper architectures and richer inputs improve positioning performance—especially in the depth-of-interaction direction, with gains of around 20%—by better exploiting light-sharing effects between slabs. Finally, we introduced a deep-learning-based signal demultiplexing approach which accurately reconstructs full 64-channel signals from reduced 16-channel measurements with structural similarity index measure above 0.98. This enables the combination of simplified hardware design crucial for data throughput together with the benefits of higher resolution positioning when using 64 signals. This work shows how deep learning, when combined with multiple signal inputs and the learned recovery of full signals from multiplexed data, can enhance the performance of PET instrumentation.