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International Journal of Biomedical Imaging
Volume 2013, Article ID 609274, 20 pages
Research Article

Learned Shrinkage Approach for Low-Dose Reconstruction in Computed Tomography

Computer Science Department, Technion - Israel Institute of Technology, Haifa 32000, Israel

Received 17 March 2013; Accepted 2 June 2013

Academic Editor: Jun Zhao

Copyright © 2013 Joseph Shtok et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We propose a direct nonlinear reconstruction algorithm for Computed Tomography (CT), designed to handle low-dose measurements. It involves the filtered back-projection and adaptive nonlinear filtering in both the projection and the image domains. The filter is an extension of the learned shrinkage method by Hel-Or and Shaked to the case of indirect observations. The shrinkage functions are learned using a training set of reference CT images. The optimization is performed with respect to an error functional in the image domain that combines the mean square error with a gradient-based penalty, promoting image sharpness. Our numerical simulations indicate that the proposed algorithm can manage well with noisy measurements, allowing a dose reduction by a factor of 4, while reducing noise and streak artifacts in the FBP reconstruction, comparable to the performance of a statistically based iterative algorithm.