**Keywords:**

A major challenge in X-ray computed tomography (CT) is to reduce radiation dose while maintaining high quality of reconstructed images. To reduce the radiation dose, one can reduce the number of projection views (sparse-view CT); however, it becomes difﬁcult to achieve high quality image reconstruction as the number of projection views decreases. Researchers have shed light on applying the concept of learning sparse representations from (high-quality) CT image dataset to the sparse-view CT reconstruction. We propose a new statistical CT reconstruction model that combines penalized weighted-least squares (PWLS) and `1 regularization with learned sparsifying transform (PWLS-ST-`1), and an algorithm for PWLS-ST-`1. Numerical experiments for sparse-view CT show that our model signiﬁcantly improves the sharpness of edges of reconstructed images compared to the CT reconstruction methods using edgepreserving hyperbola regularizer and `2 regularization with learned ST.

- ll Yong Chun
- The University of Michigan, USA

- Yong Long
- University of Michigan - Shanghai Jiao Tong University Joint Institute, China

- Xuehang Zheng
- University of Michigan - Shanghai Jiao Tong University Joint Institute, China

- Jeffrey Fessler
- The University of Michigan, USA

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