Image　quality　assessment　is　widely　used　in　many　image　processing　tasks,　which　can　help　researchers　adjust　image　processing　algorithms,　design　imaging　systems,　and　evaluate　image　processing　systems.　Generally,　CT　image　quality　assessment　can　be　categorized　into　task-specific　and　general　image　quality　evaluation.　Task-specific　image　quality　assessment　evaluates　the　performance　of　the　imaging　system　or　the　detectability　of　the　tumor.　These　IQA　index,　for　example,　are　modulation　transfer　function　(MTF),　Signal-to-Noise　Ratio　(SNR),　observer　model,　etc.　General　image　quality　assessment　measures　the　general　reconstruction　image　quality　under　different　reconstruction　algorithms.　SSIM　(Structural　Similarity),　Mean　Squared　Error　(MSE),　etc.　are　the　traditional　general　image　quality　assessment　indexes　widely　used　in　nowadays　CT　image　quality　assessment.　The　drawback　of　these　indexes　is　the　demand　for　reference　images,　which　is　not　practical　in　the　real　CT　system.　In　this　paper,　we　design　a　CT　image　dataset,　and　by　using　this　dataset,　and　we　propose　a　blind　image　quality　assessment　(BIQA)　model　based　on　CT　image　statistics,　which　can　be　employed　to　measure　the　algorithms　under　no　reference　image　situation.　Different　from　other　image　datasets,　we　recruited　no-converged　images　of　the　reconstruction　process　in　designing　datasets,　which　enables　our　BIQA　model　to　evaluate　non-converged　images　during　the　iterations.　Hence,　the　BIQA　model　can　be　embedded　in　the　reconstruction　process　to　monitor　reconstructed　image　quality　during　iterations.　©　2020　ACM.