摘要
低剂量计算机断层扫描技术具有低辐射和高效的优点,但低剂量图像伴随的噪声和伪影降低了诊断的可靠性。为了有效地改善低剂量图像的质量,通过借助小波域的优势来增强重建图像的细节纹理,结合改进的扩张卷积和亚像素技术来提高运算速度,使模型更好地部署到断层扫描设备。使用"2016AAPM低剂量图像挑战赛"数据集对提出的方法进行评估。实验结果表明,该方法重建图像的视觉效果提升明显,平均峰值信噪比比RED-CNN约高0.1428dB(1mm)、0.0939dB(3mm),在中央处理器和图形处理器上的运算速度分别提升约55%、50%以上。
Low-dose CT has the advantages of low radiation and high efficiency,but the noise and artifacts with low-dose CT images reduce the reliability of diagnosis.In order to improve the quality of low-dose CT images,this paper attempts to enhance the visuals of reconstructed CT images in the wavelet domain,and improve the running speed by combining the multi-dilated convolution and subpixel,so that the model can be better deployed to the CT equipment.The data set of"2016 AAPM Low Dose CT image Challenge"is used to evaluate the proposed method.Experimental results show that the visuals of reconstructed CT images are better.Compared with RED-CNN,the average PSNR of the proposed method is improved by 0.1428dB(1mm)/0.0939dB(3mm),and the running speed on the CPU and GPU is increased by more than 55%and 50%,respectively.
作者
李坤伦
张鲁
许宏科
宋焕生
LI Kunlun;ZHANG Lu;XU Hongke;SONG Huansheng(Educational Technology and Network Center,Chang’an University,Xi’an 710064,China;School of Electronic Control,Chang’an University,Xi’an 710064,China;School of Information Engineering,Chang’an University,Xi’an 710064,China)
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2020年第4期86-93,共8页
Journal of Xidian University
基金
国家自然科学基金(61572083)
教育部联合基金项目(6141A02022610)。
关键词
图像重建
计算机断层扫描
小波变换
卷积神经网络
image reconstruction
computed tomography
wavelet transforms
convolutional neural networks
