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This paper presents a framework for simulating and reconstructing radiopharmaceutical images using a Compton camera (CC) for potential use in personalized dosimetry. We developed a Geant4-based Monte Carlo simulation model of a two-stage CC and a Technetium-99m (Tc-99m) source, simulating gamma-ray emissions and their interactions across multiple camera orientations. This synthetic data was used to evaluate two 3D image reconstruction algorithms: simple back projection (SBP) and a kernel weighted back projection (KWBP) method. While KWBP demonstrated superiority over SBP by producing reconstructions with significantly less noise and better-preserved source geometry, both methods were limited by artifacts and blurring due to restricted angular sampling. To address these limitations, we integrated a deep learning-based image enhancement step into the reconstruction pipeline. We trained and evaluated two convolutional neural networks, a REDCNN and a U-Net, to denoise the reconstructed images. Our results show that both networks effectively suppressed noise, but the U-Net, trained with a hybrid mean squared error (MSE) and structural similarity index measure (SSIM) loss function, delivered superior performance. Quantitative analysis on a heldout test set showed that the U-Net achieved a lower average MSE of 0.0041, compared to the RED-CNN's 0.0103. Furthermore, UNet qualitatively outperformed the RED-CNN by more accurately preserving the structural integrity and shape of the sources. These findings establish a two-step strategy-combining physics-based reconstruction with data-driven denoising-as a promising pathway for refining Compton camera images for clinical applications.