A Stopping Criterion for iterative reconstruction of X-ray computed tomography

Substantial researches have shown that the wildly used statistical iterative reconstruction (SIR) methods without strong constraints, for example, the maximum likelihood estimation, could induce excessive noise into reconstructions. The noise significantly degrades image quality. In this case, the traditional method of iterating till convergence is no longer feasible. In this work, we propose a structural similarity index (SSIM) based stopping criterion for SIR. We define an indicator, referred as mSSIM, of the turning point of noise amplification based on SSIM map of reconstructed images from two adjacent iterations. The mSSIM is computed from the average of SSIM map within regions of interest (ROI). A threshold of the mSSIM is set to be the stopping criterion of iterative reconstruction. We applied this strategy to the cases of two different data noise models and iterative step sizes. Experimental tests are done on two practical datasets. Result shows that we could successfully and stably obtain images of similar quality by applying this SSIM-based stopping criterion in different cases.