基于可变阶变分模型的医用低剂量CT图像去噪

为了降低患者的辐射风险，低剂量CT（LDCT）广泛用于临床诊断，但辐射剂量的减少在重建的LDCT图像中引入了斑点噪声和条纹伪影。为了提高LDCT图像的质量，提出了一种基于可变阶变分模型的后处理技术。所提出的变分模型使用边缘指示器控制变分阶数，根据图像的特征在一阶全变分（TV）正则项和二阶有界Hessian（BH）正则项之间交替变换。采用基于快速傅里叶变换（FFT）的分裂Bregman算法求解所提出的变分模型。该模型在保留高剂量CT（HDCT）图像相应结构的同时，有效抑制了斑点噪声和条纹伪影。重建的图像和实验数据表明，所提出的变分模型比现有的先进模型具有更好的质量。 

Medical low-dose CT image denoising based on variable order variational model

Low-dose CT (LDCT) is widely used for clinical diagnosis to reduce radiation risk to patients. However, the radiation dose reduction introduces mottle noise and streak artifacts into the reconstructed LDCT images. In this paper, a post-processing technique is proposed based on variable order variational model to improve the LDCT image quality. The proposed variational model employs the edge indicator to control the order of variation, which can alternate between the first order total variation (TV) regularizer and second order bounded Hessian(BH) regularizer based on the image feature. Moreover, the proposed model is solved by split Bregman algorithm based on fast Fourier transform (FFT). The proposed model effectively suppresses mottle noise and streak artifacts, meanwhile preserving structure in reference to high-dose CT (HDCT) images. The reconstructed images and experimental data indicate that the proposed model has better quality than some existing state-of-the-art models.