Abstract
The computed tomography (CT) is considered as a significant imaging tool for the clinical diagnosis. Due to the low-dose radiation in the CT, the projection data is highly affected by the Gaussian noise. Thus, there is a demand of a framework that can eliminate the noise and provide high-quality images. This paper presents a new statistical image reconstruction algorithm by proposing a suitable regularization method. The proposed framework is the combination of two basic terms, namely data fidelity and regularization. Minimizing the log-likelihood gives data fidelity term, which represents the distribution of noise in low-dose X-ray CT images. Maximum likelihood expectation maximization algorithm is introduced as a data fidelity term. The ill-posedness problem of data fidelity term is overcome with the help of complex diffusion filter. It is introduced as regularization term into the proposed framework that minimizes the noise without blurring edges and preserving the fine structure information into the reconstructed image. The proposed model has been evaluated on both simulated and real standard thorax phantoms. The final results are compared with the various other methods, and it is analyzed that the proposed model has many desirable properties such as better noise robustness, less computational cost, enhanced denoising effect.
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Kaur, K., Tiwari, S. (2019). Low-Dose CT Image Reconstruction Using Complex Diffusion Regularization. In: Verma, N., Ghosh, A. (eds) Computational Intelligence: Theories, Applications and Future Directions - Volume II. Advances in Intelligent Systems and Computing, vol 799. Springer, Singapore. https://doi.org/10.1007/978-981-13-1135-2_50
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DOI: https://doi.org/10.1007/978-981-13-1135-2_50
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