基于噪声水平估计的多孔准直X射线荧光CT去噪算法  

Denoising Algorithm of Multi-Pinhole Collimated X-Ray Fluorescence CT Based on Noise Level Estimation

作　　者：赵如歌 冯鹏[1,2] 罗燕 张颂 何鹏[1,2] 刘亚楠[3] Zhao Ruge;Feng Peng;Luo Yan;Zhang Song;He Peng;Liu Yanan(Key Lab of Optoelectronic Technology&Systems,Ministry of Education,Chongqing University,Chongqing 400044,China;ICT NDT Engineering Research Center,Ministry of Education,Chongqing University,Chongqing 400044,China;School of Electronics and Information Engineering,Chongqing Technology and Business Institute,Chongqing 400032,China)

机构地区：[1]重庆大学光电技术及系统教育部重点实验室,重庆400044 [2]重庆大学工业CT无损检测教育部工程研究中心,重庆400044 [3]重庆工商职业学院电子信息工程学院,重庆400032

出　　处：《光学学报》2023年第20期306-315,共10页Acta Optica Sinica

基　　金：重庆市科委基础研究与前沿探索专项(cstc2020jcyj-msxmX0553);重庆市科委技术创新与应用发展专项(cstc2021jscx-gksbX0056);重庆市教委科研项目(KJQN201904007);重庆市研究生科研创新项目(CYB21059)。

摘　　要：X射线荧光CT(XFCT)是X射线CT与X射线荧光分析相结合的新型成像方式,可用于探测被修饰后的纳米金颗粒在肿瘤内部的分布及质量分数,在早期癌症诊疗方面具有较好的应用潜力。如何抑制XFCT成像的康普顿散射噪声是当前的热点问题。本文基于深度学习方法,通过卷积神经网络学习图像中的噪声分布规律,从而抑制噪声。基于此,提出了一种基于噪声水平估计和卷积神经网络的XFCT去噪网络(NeCNN)算法,该算法运用噪声估计子网络及去噪主网络进行去噪。估计子网络通过去噪卷积神经网络(DnCNN)估计噪声水平并初步降噪,随后将估计结果输入去噪主网络——全卷积神经网络(FCN)用于学习康普顿散射的分布规律,同时为兼顾局部与全局最优解采用均方误差(MSE)及结构相似度(SSIM)作为损失函数。数据集通过Geant4软件模拟扫描填充各种金属纳米颗粒(Au、Bi、Ru、Gd)的空气模体及聚甲基丙烯酸甲酯(PMMA)模体来获取,且设置不同入射X射线的强度,以此模拟不同噪声水平,增强模型泛化能力。实验结果表明,与三维块匹配滤波(BM3D)及DnCNN算法相比,NeCNN算法的去噪结果最优,其SSIM为0.95066,峰值信噪比(PSNR)为29.01558,图像质量提高最为显著。Objective X-ray fluorescence CT(XFCT)is a novel imaging modality that combines X-ray CT with X-ray fluorescence analysis(XRFA)and can be employed to probe the distribution and concentration of functionalized gold nanoparticles inside the tumor.It has good potential in the diagnosis and treatment of early-stage cancers.How to suppress Compton scattering noise for XFCT imaging is a current hotspot.Traditional denoising methods include the background fitting method,scanning phase subtraction,and iterative denoising method.Deep learning-based denoising and reconstruction methods can utilize the powerful feature learning ability of deep learning without priori information such as parameters of imaging systems,which can effectively reduce the background noise and obtain sound imaging quality.Methods We propose an XFCT denoising algorithm based on noise level estimation and convolutional neural networks(NeCNN),which consists of noise estimation subnetworks and main denoising networks(Fig.2).The estimated subnetwork estimates the noise level and reduces the preliminary noise through the denoising convolutional neural network(DnCNN).The estimated results are input into the fully convolutional neural network(FCN)and the output is adopted to learn the Compton scattering distribution.Meanwhile,as the FCN integrates a deconvolution module,the denoising and reconstruction of end-to-end fluorescence CT images can be directly achieved.We utilize the air-loaded phantoms for pretraining,while the related parameters are transferred into the PMMA phantoms to simulate the human tissue and achieve faster convergence.This two-level network structure is not a simple cascade,and the input-output and hyper parameter settings between two-level networks are linked to each other.With preliminary noise level estimation and input into the secondary network as priori information,there is a superior denoising effect compared with a single denoising network.Additionally,the mean square error(MSE)and structure similarity(SSIM)are employed as the l

关 键 词：X射线荧光CT 康普顿散射 噪声估计 NeCNN算法 

分 类 号：O434.1[机械工程—光学工程]