Application of a non-convex smooth hard threshold regularizer to sparse-view CT image reconstruction

Sean Rose; Emil Y. Sidky; Xioachuan Pan

doi:10.1117/12.2082116

18 March 2015 Application of a non-convex smooth hard threshold regularizer to sparse-view CT image reconstruction

Sean Rose, Emil Y. Sidky, Xioachuan Pan

Author Affiliations +

Proceedings Volume 9412, Medical Imaging 2015: Physics of Medical Imaging; 941206 (2015) https://doi.org/10.1117/12.2082116
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

In this work, we apply non-convex, sparsity exploiting regularization techniques to image reconstruction in computed tomography (CT).We modify the well-known total variation (TV) penalty to use a non-convex smooth hard threshold (SHT) penalty as opposed to the typical ℓ₁ norm. The SHT penalty is different from the p <1 norms in that it is bounded above and has bounded gradient as its argument approaches the zero vector. We propose a re-weighting scheme utilizing the Chambolle-Pock (CP) algorithm in an attempt to solve a data-error constrained optimization problem utilizing the SHT penalty and call the resulting algorithm SHTCP. We then demonstrate the algorithm on sparse-view reconstruction of a simulated breast phantom with noiseless and noisy data and compare the converged images to those generated by a CP algorithm solving the analogous data-error constrained problem utilizing the TV. We demonstrate that SHTCP allows for more accurate reconstruction in the case of sparse-view noisy data and, in the case of noiseless data, allows for accurate reconstruction from fewer views than its TV counterpart.

Citation Download Citation

Sean Rose, Emil Y. Sidky, and Xioachuan Pan "Application of a non-convex smooth hard threshold regularizer to sparse-view CT image reconstruction", Proc. SPIE 9412, Medical Imaging 2015: Physics of Medical Imaging, 941206 (18 March 2015); https://doi.org/10.1117/12.2082116

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