摘要
针对锥形束CT(CBCT)图像质量较差的问题,提出一种基于卷积神经网络的超分辨率重建(SRCNN)方法,旨在提高CBCT图像的分辨率。本研究分别对头颈、盆腔、胸部的CBCT图像进行研究,先使用非局部均值(NLM)方法对图像进行降噪处理,再分别使用双三次插值重建(BIC)方法和SRCNN重建方法进行超分辨率重建。结果表明,BIC方法和SRCNN重建方法均能提高CBCT图像的分辨率,SRCNN重建方法较BIC方法有更高的峰值信噪比,而在结构相似度和特征相似度上,BIC方法和SRCNN重建方法的差别不大。从图像峰值信噪比及特征相似度上看,此方法对盆腔部CBCT图像处理效果更为显著,对头颈部及胸部处理效果相近。
A reconstruction method based on super-resolution convolutional neural network(SRCNN)is proposed to solve the problem of poor cone-beam computed tomography(CBCT)image quality,thereby improving the resolution of CBCT image.The CBCT images of head and neck,pelvic cavity and thorax were researched.Firstly,image noises were removed by non-local means method,and then super-resolution reconstruction is carried out by bicubic interpolation(BIC)and SRCNN,separately.The results show that both BIC method and SRCNN method can improve the resolution of CBCT image.The peak signal-to-noise ratio(PSNR)obtained by SRCNN method is higher than that obtained by BIC method,but the differences in structural similarity(SSIM)and feature similarity(FSIM)between SRCNN method and BIC method are trivial.The analysis on PSNR and FSIM shows that SRCNN method has more remarkable effect on the improvement of pelvic CBCT image,and the effects on the improvements of head and neck CBCT image and thoracic CBCT image are similar.
作者
邓春燕
陆佳扬
黄宝添
DENG Chunyan;LU Jiayang;HUANG Baotian(Department of Radiation Oncology,Cancer Hospital of Shantou University Medical College,Shantou 515000,China)
出处
《中国医学物理学杂志》
CSCD
2020年第7期878-882,共5页
Chinese Journal of Medical Physics
基金
国家自然科学基金(81602667)
广东省科技创新战略专项资金(纵向协同管理方向)[汕府科(2018)157号]。
关键词
锥形束CT
卷积神经网络
降噪
超分辨率重建
cone-beam computed tomography
convolutional neural network
denoise
super-resolution construction
