Python-Driven CT Scan Image Refinement: A Study on Reconstruction Techniques

This paper has implemented Python programming in the field of computational imaging to further enhance the image quality of CT scans. This is done to improve the clinical efficacy of these diagnostic tools. At its core, this paper consists of a multi-stage algorithmic pipeline which is used to address commonly found image artifacts and limitations: Fourier-domain filtering to mitigate the high-frequency noise; Radon transform-based technique to remove streak artifacts; unsharp mask algorithm to sharpen the image further and finally implementing the Wiener deconvolution to achieve a high resolution image.