Sparse view sampling and reducing current of X-ray source can effectively reduce radiation dose of multispectral CT,but it will make the projection data insufficient and noisy,leading to serious degeneration of the reconstructed images.To address this problem,we extend the traditional total nuclear variation(TNV) and propose the nonlocal total nuclear variation(NLTNV) regularization method by employing the low rank property of Jacobian matrix composed of nonlocal gradient vector.The proposed method uses only one regularization term to model three kinds of prior information(the structural similarity along energy dimension,the sparsity of image gradient and the spatial nonlocal self-similarity) to restore image details in low dose case,which can effectively alleviate the problem of using too many regularization parameters in reconstruction model,caused by employing multiple independent regularization terms to model different prior information of multispectral CT image.In addition,the reconstruction model based on NLTNV is a convex model,which guarantees the stability and convergence of the algorithm.The experimental results show that compared with the TNV regularization method,the proposed method can significantly improve the overall quality of the reconstructed images.

objective:To explore the value of non-enhanced CT images and enhanced CT image texture analysis in differentiating bladder papilloma from bladder cancer. Methods:64 pathologically confirmed cases of benign and malignant bladder tumors in the third Affiliated Hospital of Anhui Medical University from January 2016 to January 2020 were retrospectively included, including 32 cases of benign lesions and 32 cases of malignant lesions. All patients underwent phase III dynamic enhanced CT scan, and their non-enhanced and arterial phase images were selected for study. MaZda texture analysis software was used to extract texture features of bladder lesions on CT non-enhanced and arterial phase images. Fisher method, the minimum classification error and the minimum average correlation coefficient method(POE + ACC) and the related information measurement method(MI) were selected to identification of benign and malignant lesions of bladder 10 optimal texture characteristic value, Using Mazda bl1 tools of principal component analysis(PCA) and linear discriminant analysis(LDA) and nonlinear discriminant analysis(NDA) to select the best texture feature analysis, The minimum misjudgment rate(R) for differentiating benign from malignant bladder tumors was calculated. ROC test was conducted for the optimal texture parameters corresponding to the minimum misjudgment rate, and the quantifiable parameters with the most auxiliary differential significance were selected for differential diagnosis of benign from malignant bladder tumors. Results:Through the study found that in differentiating benign and malignant tumors of bladder images of omics, based on the non-enhanced CT images MI + NDA combination of misjudgment rate is the lowest(1.56%), The best texture parameters selected were wavenhh_s-1, Horzl_Fraction, Horzl_ShrtREmp, Sigma and Variance, with AUC values corresponding to(0.932, 0.897, 0.902, 0.935 and 0.849, all P values less than 0.01), Combined with these five indicators, the AUC value was 0.985, specificity 96.87% and sensitivity 96.87%, with statistically significant differences. At the artery stage, POE + ACC + NDA combination had the lowest misjudgment rate(1.56%). The best selected texture parameters are:wavenhh_s-1, wavenhh_S-2, 135 dr_ShrtREmp, GrVariance, AUC corresponding to(0.916, 0.711, 0.797, 0.793, all P values less than 0.01) The AUC value combined with these four indicators was 0.916, the specificity was 84.37%, and the sensitivity was 81.25%. The difference was statistically significant. The AUC value of the combined non-enhanced and enhanced best texture parameter analysis was 0.997, Specificity was 93.75%, sensitivity was 100%, the difference was statistically significant. Conclusion:The technique of texture analysis using CT non-enhanced and enhanced image has certain application value in the differentiation of bladder papilloma and bladder cancer.