Filtered Backprojection Reconstruction with Configurable Window Functions

In this implementation, we achieve image reconstruction through filtered backprojection by employing various window functions for frequency domain filtering. Available window functions include Hanning, Hamming, Blackman windows, among others. From a code perspective, these window functions are typically applied during the frequency filtering stage of the algorithm by multiplying the ramp filter with the selected window function in Fourier space. Additionally, we can further optimize reconstruction quality by adjusting window parameters programmatically - for instance, modifying the bandwidth and dynamic range parameters of the Hanning window through function arguments. Proper window function selection and parameter tuning in the filtering stage can significantly enhance the quality and accuracy of reconstructed images by controlling spectral leakage and improving noise characteristics. The implementation typically involves defining window functions as discrete arrays that are convolved with the projection data's Fourier transform before applying the inverse Fourier transform and backprojection operations.