## 4 Discussion  
Enhancing the spatial resolution and sensitivity in both preclinical and clinical PET scanners is one of the key motivations driving research in PET instrumentation in academia and industry. A high spatial resolution and sensitivity are among the most important performance parameters in preclinical PET imaging. A higher absolute sensitivity enables a reduction in the scanning time and the injected activity. Furthermore, higher intrinsic spatial resolution leads to the detection of smaller structures and reduces quantification bias by decreasing the partial volume effect. In this work, we proposed a novel, deep learning-based technique for the positioning of scintillation events inside of a monolithic scintillator from the optical photons' distribution map. A monolithic crystal is proposed to boost the absolute sensitivity while the MLP method used to compensate for the loss of spatial resolution caused using some regular positioning methods, like Anger logic, is based on a simple center of gravity method. This new PET detector module exploiting the MLP positioning method aims to achieve an optimal trade-off between detection efficiency and spatial resolution. For a single detector module using the MLP positioning algorithm, the spatial resolution (FWHM) varies from 0.78 mm at the center to 1.15 mm at the edge of the detector. Conversely, the FWHM for a detector module using Anger logic positioning varies between 0.96 mm and 1.6 mm. A possible explanation of the improved spatial resolution by MLP is the capability of deep learning to extract various types of feature maps from an optical photon distribution map that leads to a better positioning accuracy relative to Anger logic, which is based on a simple center of mass calculation algorithm according to the weight of the SiPM pixels. The results proved that the sensitivity is independent from the positioning algorithm because it only plays a role in the treatment of optical photons and not annihilation photons. A recent study reporting on a Cherenkov-based detector used deep learning for event positioning achieved a FWHM of 1.54 mm and 1.59 mm in the XY plane and \(Z\)-axis, respectively [13]. The Jagiellonian-PET (J-PET) is the sole whole-body PET scanner based on a monolithic crystal, an axial FOV of 1 m, a spatial resolution of approximately 3 mm, and a sensitivity of 14.9 cps/kBq at the CFOV [25]. The main purpose of this work is not to introduce a new preclinical-PET scanner with similar or better performance than the state-of-the-art commercial scanners, but rather to assess the impact of a deep learning-assisted positioning algorithm in terms of an improvement in the spatial resolution of a preclinical PET scanner equipped with a monolithic scintillation crystal without degrading the sensitivity through a minor modification of the image acquisition software. An important aspect of the proposed preclinical-PET scanner, equipped with MLP positioning, is the way the distribution of scintillation photons reaching the SiPMs can be utilized to gain data about the scintillation position in the crystal. This is the incentive motivating the evaluation of the characteristics of photon transport. The map of optical photon distributions provides a clue about the distance of the scintillation event from the SiPM surface, confirming that the width of the distribution of optical photons is linked to the depth of interaction that can be utilized to approximate the DOI. The DOI resolution for the detector module positioning with Anger logic and MLP algorithms varied from 0.66 mm and 0.42 mm at 2 mm from the SiPM's surface and increased to 1.38 and 1.02 mm for larger distances (10 mm), respectively. For scintillations taking place close to the SiPM's surface, a small number of SiPM pixels are fired and the width of the distribution map is small. However, by moving to a larger distance, a higher number of SiPM pixels are involved in positioning, thus leading to the degradation of the DOI spatial resolution.