Learned shrinkage approach for low-dose reconstruction in computed tomography

Authors:
Joseph Shtok;Michael Elad;Michael Zibulevsky
Affiliations:
Computer Science Department, Technion - Israel Institute of Technology, Haifa, Israel;Computer Science Department, Technion - Israel Institute of Technology, Haifa, Israel;Computer Science Department, Technion - Israel Institute of Technology, Haifa, Israel
Venue:
Journal of Biomedical Imaging
Year:
2013

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Abstract

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.