CT Imaging Principles with Radon Transform - From Medical Instrumentation Course

This project demonstrates CT imaging principles through Radon transform implementation, originally developed for a medical instrumentation course. CT (Computed Tomography) represents a non-invasive medical imaging technique that generates cross-sectional images of internal human structures through multiple X-ray scans from different angles combined with computational algorithms. The Radon transform serves as one of the fundamental mathematical methods in CT imaging, converting X-ray projection data into reconstructed cross-sectional images. In computational implementation, the Radon transform algorithm typically involves: - Projection data acquisition from various angles (0-180 degrees) - Line integral calculations along different paths - Back-projection or filtered back-projection reconstruction techniques Key programming considerations include: 1. Implementing discrete Radon transform using numerical integration methods 2. Applying interpolation techniques for continuous angle sampling 3. Utilizing Fourier transform properties for efficient computation 4. Implementing filtering algorithms (e.g., Ram-Lak filter) for noise reduction Through Radon transform processing of X-ray projections, the system extracts directional projection information to reconstruct internal tissue structures and distribution patterns, enabling physicians to make accurate diagnoses and treatment plans. The algorithm's efficiency is crucial for clinical applications, requiring optimization for both accuracy and computational speed.