Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing
Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing
复制标题
使用基于快速字典学习的处理来改善腹部肿瘤低剂量 CT 图像
DOI：
10.1088/0031-9155/58/16/5803
复制
发表时间：
2013-08-21
影响因子：
3.5
通讯作者：
Toumoulin, Christine
中科院分区：
文献类型：
--
作者：
Chen, Yang;Yin, Xindao;Toumoulin, Christine
在腹部计算机断层扫描（CT）中，对于接受CT图像引导的手术或放射治疗的癌症患者，重复辐射暴露通常是不可避免的。因此，应考虑低剂量扫描，以避免累积X射线辐射的危害。这项工作的目的是改善腹部肿瘤CT图像低剂量扫描，通过使用快速字典学习（DL）为基础的处理。基于稀疏表示理论，提出的基于块的DL方法可以有效地抑制斑点噪声和条纹伪影。对临床数据进行的实验表明，所提出的方法带来了令人鼓舞的改善腹部低剂量CT图像与肿瘤。
In abdomen computed tomography (CT), repeated radiation exposures are often inevitable for cancer patients who receive surgery or radiotherapy guided by CT images. Low-dose scans should thus be considered in order to avoid the harm of accumulative x-ray radiation. This work is aimed at improving abdomen tumor CT images from low-dose scans by using a fast dictionary learning (DL) based processing. Stemming from sparse representation theory, the proposed patch-based DL approach allows effective suppression of both mottled noise and streak artifacts. The experiments carried out on clinical data show that the proposed method brings encouraging improvements in abdomen low-dose CT images with tumors.