Fourth-order partial differential equations based anisotropic diffusion model for low-dose CT images

The low-dose X-ray Computed Tomography (CT) is one of the most effective and indispensable imaging tools for clinical diagnosis. The reduced number of photons in low-dose X-ray CT imaging introduces the vulnerability towards Poisson and Gaussian noise. The majority of research till date focuses on reconstructing the images by reducing the effect of either Poisson or Gaussian noise. Thus, there is a need for a reconstruction framework that can counter the effects of both types of noises simultaneously. In this paper, an approach is proposed to handle the mixed noise (i.e. Poisson and Gaussian noises). Variational framework is utilized as energy minimization function. Minimizing the log likelihood gives data-fidelity term which portrays the distribution of noise in low-dose X-ray CT images. The problem of data-fidelity term as well as mixed noise issue in the sinogram data is resolved simultaneously by proposing a novel filter. The proposed filter modifies the Anisotropic Diffusion (AD) model based on Convolution Virtual Electric Field AD called as MADC. The modification in AD is achieved by applying fourth-order partial differential equations. To evaluate the effectiveness of the proposed MADC technique, both qualitative and quantitative evaluations are performed on three simulated test phantoms and one real standard thorax phantom of size 128 x 128. Afterwards, the performance of the proposed technique is compared with competitive denoising techniques. The experimental results reveal that the proposed framework significantly preserves the edges of reconstructed images and introduces lesser number of gradient reversal artifacts.