As the rapid growth of CT based medical application, low-dose CT reconstruction becomes more and more important to
human health. Compared with other methods, statistical iterative reconstruction (SIR) usually performs better in lowdose
case. However, the reconstructed image quality of SIR highly depends on the prior based regularization due to the
insufficient of low-dose data. The frequently-used regularization is developed from pixel based prior, such as the
smoothness between adjacent pixels. This kind of pixel based constraint cannot distinguish noise and structures
effectively. Recently, patch based methods, such as dictionary learning and non-local means filtering, have outperformed
the conventional pixel based methods. Patch is a small area of image, which expresses structural information of image.
In this paper, we propose to use patch based constraint to improve the image quality of low-dose CT reconstruction. In
the SIR framework, both patch based sparsity and similarity are considered in the regularization term. On one hand,
patch based sparsity is addressed by sparse representation and dictionary learning methods, on the other hand, patch
based similarity is addressed by non-local means filtering method. We conducted a real data experiment to evaluate the
proposed method. The experimental results validate this method can lead to better image with less noise and more detail
than other methods in low-count and few-views cases.