Aims and Scope: Computed tomography (CT) is one of the most efficient clinical diagnostic
tools. The main goal of CT is to reproduce an acceptable reconstructed image of an object
(either anatomical or functional behaviour) with the help of a limited set of projections at different
Background: To achieve this goal, one of the most commonly iterative reconstruction algorithm
called Maximum Likelihood Expectation Maximization (MLEM) is used.
Objective: The conventional Maximum Likelihood (ML) algorithm can achieve quality images in
CT. However, it still suffers from optimal smoothing as the number of iterations increases.
Methods: For solving this problem, this paper presents a novel statistical image reconstruction algorithm
for CT, which utilizes a nonlocal means of fuzzy complex diffusion as a regularization term
for noise reduction and edge preservation.
Results: The proposed model was evaluated on four test cases phantoms.
Conclusion: Qualitative and quantitative analyses indicate that the proposed technique has higher
efficiency for computed tomography. The proposed method yields significant improvements when
compared with the state-of-the-art techniques.