  Achieving high-quality reconstructions from low-dose computed tomography
(LDCT) measurements is of much importance in clinical settings. Model-based
image reconstruction methods have been proven to be effective in removing
artifacts in LDCT. In this work, we propose an approach to learn a rich
two-layer clustering-based sparsifying transform model (MCST2), where image
patches and their subsequent feature maps (filter residuals) are clustered into
groups with different learned sparsifying filters per group. We investigate a
penalized weighted least squares (PWLS) approach for LDCT reconstruction
incorporating learned MCST2 priors. Experimental results show the superior
performance of the proposed PWLS-MCST2 approach compared to other related
recent schemes.
