中国医疗设备2018，Vol.33Issue(6)：12-15,20,5.DOI:10.3969/j.issn.1674-1633.2018.06.003
基于加权字典学习方法的低剂量CT图像重建
Low-Dose CT Reconstruction via Weighted Dictionary Learning Method
摘要
Abstract
To prevent the patients from the overdose of X-ray radiation, the radiation dose should be reduced in the design of the CT scanning device. The dictionary learning reconstruction algorithm, which is derived from the compressed sensing theory, is able to make high quality recovery from the under-sampled scanning data. However, the regularization term of this algorithm is not able to distinguish the noise and the low-contrast information, tending to lose the soft tissue edge details. This article proposed a weighted dictionary learning method, which calculated the weight factors of the regularization term based on the residuals of the image patches by subtracting the dictionary sparse representation after each iterative step. The weight factors helped preserve structural information of the image and smooth the noise during the iterative process by changing the smooth effects upon different regions of the image. The corresponding experiments proves that the proposed algorithm preserves the soft tissue edge details efficiently. The quality of the reconstruction image is improved compared with the existing dictionary learning method.关键词
图像处理/CT重建/字典学习/权重因子/欠采样Key words
image processing/CT reconstruction/dictionary learning/weight factor/under-sampled分类
数理科学引用本文复制引用
章程,张健,杜强,李铭,刘景鑫..基于加权字典学习方法的低剂量CT图像重建[J].中国医疗设备,2018,33(6):12-15,20,5.基金项目
国家重点研发计划(2016YFC0103500) （2016YFC0103500）
江苏省自然科学基金项目(BK20170392,BK20151232) （BK20170392,BK20151232）
中国科学院青年创新促进会(2014281). （2014281）
