Compton PET: A Simulation Study for a PET Module with Novel Geometry and Machine Learning for Position Decoding.

This paper describes a simulation study of a positron emission tomography (PET) detector module that can reconstruct the kinematics of Compton scattering within the scintillator. We used a layer structure, with which we could recover the positions and energies for the multiple interactions of a gamma ray in the different layers. Using the Compton scattering formalism, the sequence of interactions can be estimated. The true first interaction position extracted in the Compton scattering will help minimize the degradation of the reconstructed image resolution caused by intercrystal scatter events. Because of the layer structure, this module also has readily available user-defined resolution for the depth of interaction. The semi-monolithic crystals enable high light collection efficiency and an energy resolution of ~10% has been achieved in the simulation. We used machine learning to decode the gamma ray interaction locations, achieving an average spatial resolution of 0.40 mm. Our proposed detector design provides a pathway to increase the sensitivity of a system without affecting other key performance features.