Learned Shrinkage Approach for Low-Dose Reconstruction in Computed Tomography

Abstract EN

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.