iradon Transform Implementation

In this article, the author presents a custom implementation of the iradon transform. This transformation reconstructs images from projection data, serving as a fundamental technique in medical imaging and computer vision applications. During the development process, the implementation likely involved key computational steps including backprojection algorithms, filter design for noise reduction, and interpolation methods for handling discrete projection angles. Common challenges encountered might include optimizing reconstruction accuracy through proper ramp filtering implementation, managing computational complexity with Fourier-based approaches, and addressing artifacts through techniques like Hann or Ram-Lak filters. The implementation typically requires extensive experimentation with parameters such as projection angles spacing and interpolation methods to ensure reconstruction reliability. When applying this technique, practitioners should pay attention to critical implementation details including proper normalization of projection data, selection of appropriate filter functions based on noise characteristics, and optimization of backprojection operations through vectorization or parallel computing. Overall, the iradon transform represents a crucial and versatile technology with broad application prospects in medical diagnostics and scientific research, particularly in CT scan reconstruction and non-destructive testing scenarios.