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

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 loss function to get the local and global optimal solutions.Results and discussions The imaging system contains an X-ray source,a phantom to be measured,two sets of pinhole collimators,and two sets of fluorescence detectors(Fig.1).The distances between the fan beam X-ray source and the phantom center,between the pinhole collimator and the phantom,and between the detector and pinhole collimator are 15,5,and 5 cm respectively.The detector consists of 55×185 cadmium telluride(CdTe)detector units with an energy resolution of 0.5 keV,and the crystal size is designed to be 0.3 mm×0.3 mm.The datasets are obtained with Geant4 software by scanning air phantom and PMMA phantom in which various metal nanoparticles(Au,Bi,Ru,Gd)are filled,and different incident X-ray intensities are set to simulate different noise levels and enhance the model's generalization ability.The imaging phantom is set as a cylinder with a diameter of 3 mm and a height of 5 cm,and the settings of element concentration are divided into two types,including high mass fraction versus low mass fraction,where high mass fraction includes 0.2%,0.4%,0.6%,0.8%,1.0%,and 1.2%,and low mass fraction includes 0.1%,0.12%,0.14%,0.16%,0.18%,and 0.2%.The programming language is Python 3.6 and the NeCNN is implemented based on Pytorch 1.7.0.Meanwhile,the hardware platform is configured as Intel i5-9600kf CPU,NVIDIA Titan V(12 GB/NVIDIA)GPU,and 16 G DDR4 RAM.The hyper parameters are shown in Table 1.Figure 6 shows the denoised images with NeCNN,BM3D,and DnCNN algorithms.We can easily find that both NeCNN and DnCNN can effectively reduce noise in the background region,which is difficult to handle for the BM3D algorithm.Additionally,NeCNN is more effective than DnCNN in removing abnormal pixel spots caused by self-absorption in the center region of interest(ROI).Generally,the proposed NeCNN is quantitively and qualitatively superior to the traditional BM3D and DnCNN algorithms.The NeCNN algorithm has the largest PSNR(29.01558)and SSIM(0.95066)values.Compared with DnCNN,NeCNN shows an improvement of 0.23993 and 0.02734 in terms of PSNR and SSIM respectively.Conclusions This sduty proposes a novel denoising algorithm for XFCT images based on deep learning to estimate the Compton scattering noise level by noise estimation subnetworks and noise reduction by the denoising main network.The experimental results show that for both air and PMMA phantoms,the PSNR and SSIM of images with NeCNN are both higher than DnCNN and BM3D.This illustrates the effectiveness of the proposed algorithm and shows its potential to be applied in practical imaging systems in the future.