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A Rigid Motion Artifact Reduction Method for CT Based on Blind Deconvolution
Open AccessArticle

A Novel Blind Restoration and Reconstruction Approach for CT Images Based on Sparse Representation and Hierarchical Bayesian-MAP

1
School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China
2
School of Electrical Automation and Information Engineering, Tianjin University, Tianjin 300072, China
*
Author to whom correspondence should be addressed.
Algorithms 2019, 12(8), 174; https://doi.org/10.3390/a12080174
Received: 29 May 2019 / Revised: 11 August 2019 / Accepted: 14 August 2019 / Published: 16 August 2019
(This article belongs to the Special Issue The Second Symposium on Machine Intelligence and Data Analytics)
Computed tomography (CT) image reconstruction and restoration are very important in medical image processing, and are associated together to be an inverse problem. Image iterative reconstruction is a key tool to increase the applicability of CT imaging and reduce radiation dose. Nevertheless, traditional image iterative reconstruction methods are limited by the sampling theorem and also the blurring of projection data will propagate unhampered artifact in the reconstructed image. To overcome these problems, image restoration techniques should be developed to accurately correct a wide variety of image degrading effects in order to effectively improve image reconstruction. In this paper, a blind image restoration technique is embedded in the compressive sensing CT image reconstruction, which can result in a high-quality reconstruction image using fewer projection data. Because a small amount of data can be obtained by radiation in a shorter time, high-quality image reconstruction with less data is equivalent to reducing radiation dose. Technically, both the blurring process and the sparse representation of the sharp CT image are first modeled as a serial of parameters. The sharp CT image will be obtained from the estimated sparse representation. Then, the model parameters are estimated by a hierarchical Bayesian maximum posteriori formulation. Finally, the estimated model parameters are optimized to obtain the final image reconstruction. We demonstrate the effectiveness of the proposed method with the simulation experiments in terms of the peak signal to noise ratio (PSNR), and structural similarity index (SSIM). View Full-Text
Keywords: CT reconstruction; compressed sensing; blind restoration; hierarchical Bayesian-MAP CT reconstruction; compressed sensing; blind restoration; hierarchical Bayesian-MAP
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Sun, Y.; Zhang, L.; Li, Y.; Meng, J. A Novel Blind Restoration and Reconstruction Approach for CT Images Based on Sparse Representation and Hierarchical Bayesian-MAP. Algorithms 2019, 12, 174.

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