The resolution of medical images inherently limits the diagnostic value of clinical image acquisitions. Obtaining high-resolution images through tomographic imaging modalities like Computed Tomography (CT) requires high radiation doses, which pose health risks to living subjects.
The main focus of this thesis is to develop a unified deep learning pipeline for enhancing the spatial resolution of low-dose CT scans by refining both the sinogram (projection) domain and the reconstructed image domain. Leveraging the Swin Transformer architecture, the proposed approach aims to generate high-resolution (HR) scans with improved anatomical detail preservation, while significantly reducing radiation dose requirements.