基于区域内容感知核范数的低剂量CT影像去噪

针对传统基于核范数最小化（NNM）的低秩约束模型在低剂量CT（LDCT）影像去噪中易造成局部纹理细节丢失的问题，提出一种具有区域内容感知能力的加权NNM的LDCT影像去噪算法。首先采用基于奇异值分解（SVD）的方法估计LDCT影像中的局部噪声强度；然后采用基于局部统计特性的方法进行目标影像块匹配；最后根据影像局部噪声强度以及不同奇异值水平自适应设置核范数权重，以实现基于加权NNM的LDCT影像去噪。仿真实验结果表明，所提算法在均方根误差（RMSE）指标上较传统NNM算法、全变分最小化算法以及变换学习算法分别降低30.11%、14.38%和8.75%，在结构相似度（SSIM）指标上较上述3种算法分别提高34.24%、23.06%和11.52%。真实临床数据实验结果表明，所提算法处理结果的放射医生评价平均分为8.94，与常规剂量CT影像的评价平均分数仅差0.21，显著高于传统NNM算法、全变分最小化算法和变换学习算法的平均分。仿真及真实临床数据的实验结果表明，所提算法能够在滤除LDCT影像伪影噪声的同时，有效保持局部纹理细节信息。

Regional-content-aware nuclear norm for low-does CT image denosing

The low-rank constraint model based on traditional Nuclear Norm Minimization(NNM)tends to cause local texture detail loss in the denoising of Low-Dose CT(LDCT)image. To tackle this issue,a regional-content-aware weighted NNM algorithm was proposed for LDCT image denoising. Firstly,a Singular Value Decomposition(SVD)based method was proposed to estimate the local noise intensity in LDCT image. Then,the target image block matching was performed based on the local statistical characteristics. Finally,the weights of the nuclear norms were adaptively set based on both the local noise intensity of the image and the different singular value levels,and the weighted NNM based LDCT image denoising was realized. The simulation results illustrated that the proposed algorithm decreased the Root Mean Square Error(RMSE)index by 30. 11%,14. 38% and 8. 75% respectively compared with the traditional NNM,total variation minimization and transform learning algorithms,and improved the Structural SIMilarity(SSIM)index by 34. 24%,23. 06% and 11. 52% respectively compared with the above three algorithms. The experimental results on real clinical data illustrated that the mean value of the radiologists' scores of the results obtained by the proposed algorithm was 8. 94,which is only 0. 21 lower than that of the corresponding full dose CT images,and was significantly higher than those of the traditional NNM,total variation minimization and transform learning algorithms. The simulation and clinical experimental results indicate that the proposed algorithm can effectively reduce the artifact noise while preserving the texture detail information in LDCT images.