## 1 Introduction  
In nuclear medicine, there is a functional medical imaging technique called Positron Emission Tomography (PET). This functional medical imaging technique is particularly useful in obtaining information from biological (or functional) processes, such as metabolism, blood flow, absorption of pharmaceuticals, etc. In order to obtain biological information, we need a compound that will be injected into the patient's body. These compounds are referred to as radiotracers, chemical compounds in which one or several atoms are replaced by a radioisotope, an element with an unstable nucleus. In the case of PET, the most widely used radiotracer is fluorodeoxyglucose F18 (FDG) [1, 2]. This molecule is analogous to a glucose molecule with the replacement of a hydroxyl radical with the radionuclide fluorine-18, which emits positrons through a \(\beta^{+}\) decay. This type of functional imaging is extensively used in oncology applications, as tumors or cancerous cells consume vast quantities of glucose to enable their uncontrolled reproduction. Therefore, when a patient receives a radiotracer dose, it is usually detected in the area where the cancerous cells are located. As a consequence of this phenomenon, those areas are easily detected by a PET scanner as regions with high activity concentrations, also known as hot regions, due to the radioactivity of the radiotracer. The physical process that PET relies on is the detection of two 511 KeV photons generated from the matter-antimatter annihilation of an electron with a positron. In the data acquisition step of a PET scanner, those photons are detected at an angle at 180\({}^{\circ}\) forming a line called the Line Of Response (LOR), which defines the path followed by the pair of detected photons emitted in the annihilation event. In order to calibrate a PET scanner, a scan with an imaging phantom (or just phantom) is needed. An imaging phantom is a device specifically designed to simulate a standardized human body or part of it and imitate the response of human tissues and organs when injected with FDG. Therefore, imaging phantoms are used to analyze, evaluate, and tune PET scanners. To that effect, a series of tests, of which large part have to be performed on the reconstructed image, are specified by the NEMA [3; 4; 5] protocols. The raw data obtained from a PET scan must be processed to obtain the required reconstructed image. Different methods exist to perform this reconstruction. There are simple, non-iterative methods, such as Inverse Radon Transform [6] or Filtered Back Projection (FBP) [7]. These methods perform reasonably well, but the results are usually not of high quality. There are also more complex iterative reconstruction methods, such as Maximum Likelihood Expectation Maximization (MLEM) [8] or Ordered Subset Expectation Maximization (OSEM) [9]. These methods are usually computationally expensive but result in high-quality reconstructions of the patient or phantom used in scan acquisition. The rapid and successful advancements in the field of Artificial Intelligence (AI) that have been enabled by the use of neural networks (NNs) have also been adopted in the medical imaging field. In state-of-the-art PET imaging reconstruction, the use of convolutional neural networks (CNNs) has delivered good results in the reconstruction of images from data acquired with PET scans as input for the neural network [10; 11]. The use of neural networks has some caveats: they are prone to overfitting, they are black-box models, and they require a large number of data during the training step. In the case of PET image reconstruction, a large quantity of images is needed in order to train CNNs to perform the reconstruction task. There are two ways to acquire PET data and images for studies. The first involves the acquisition of real scans from clinical cases, PET data repositories, or data from experimental scanners.