Study on an Improved Filtered Back-projection Image Reconstruction Algorithm Combined with Wavelet Denoising

In the process of medical imaging (MI) reconstruction, filtering of original projection data is a key step to overcome artifact of the reconstructed image. Although some classical filters can be used into FBP algorithm, some drawbacks limit its application in practice, especially for the data polluted by non-stationary random noises. To overcome the shortcomings of these traditional filtering, an improved FBP combined with a shift-invariant wavelet threshold denoising algorithm is proposed in this paper. In the experiments, the reconstructed effects were compared between the improved algorithm, classical soft and hard threshold denoising methods. Experimental results illustrated that the reconstruction effect of improved FBP algorithm is better than that of others. In addition, two evaluation standards, i.e. mean-square error (MSE), peak-to-peak signal-noise ratio (PSNR) were used to compare the results of different algorithms. It was found that the reconstructed effects of the improved FBP combined with shift-invariant wavelet hard threshold function based on RL filter is better than others. Therefore, this improved FBP algorithm has potential value in the medical imaging.