Abstract
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 angles.
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.
Keywords: Computed tomography, maximum likelihood, noise reduction, fuzzy logic, nonlocal means, complex diffusion.
Graphical Abstract
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