Abstract

Computed Tomography (CT) has evolved from simple axial image acquisition to a sophisticated modality capable of generating high-resolution volumetric and three-dimensional (3D) models. Image reconstruction—the process of converting raw projection data into diagnostic images—forms the foundation of CT imaging. Modern advancements, including iterative algorithms, Fourier-based techniques, and AI-enhanced segmentation, have significantly improved image quality, reduced noise, and enabled complex 3D visualization. This review provides a comprehensive overview of CT image reconstruction principles, algorithms, clinical applications of 3D reconstruction, the role of artificial intelligence (AI), limitations, and future directions in CT imaging.

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