摘要
光学相干层析成像(OCT)的质量通常会受到固有散斑噪声和低采样率的影响。为了在短扫描时间内获得高信噪比和高分辨率的OCT图像,本文提出了一种改进的OCT图像超分辨率重建网络模型PPECA-SRGAN。该模型将生成对抗网络(GAN)作为基础结构,可以不依赖配对数据集进行训练。在该模型的生成器残差块之间添加了金字塔注意力模块PANet,同时在判别器中加入了本文新提出的PECA模块,使其更加注重捕捉图像细节,提升模型对图像边缘纹理的重建能力。将所提PPECA-SRGAN模型在OCT图像数据集上进行实验,得到的峰值信噪比和结构相似性指标的平均值较当前三种经典模型的平均值分别约提高了3.5%和5.6%。实验结果表明,所提模型在鲁棒性和OCT图像细节重建方面较经典模型有较大提升。
Objective Optical coherence tomography(OCT)imaging shows great potential in clinical practice because of its n oninvasive nature.However,two critical issues affect the diagnostic capability of OCT imaging.The first problem is that the interferential nature of OCT imaging produces interference noise,which reduces contrast and obfuscates fine structural features.The second problem is caused by the low spatial sampling rate of OCT.In fact,in clinical diagnosis,the use of a lower spatial sampling rate is a method to achieve a wide field of vision and reduce the impact of unconscious movement.Therefore,most OCT images obtained in reality are not optimal in terms of signal-to-noise ratio and spatial sampling rate.There are significant differences in the texture and brightness of the retinal layer in patients,as well as in the shape and size of the lesion area,so traditional models may not be able to reliably reconstruct the pathological structure.To obtain high peak signal-to-noise ratio(PSNR)and high-resolution B-scan OCT images,it is necessary to develop sufficient methods for super-resolution reconstruction of OCT images.In this paper,an improved OCT superresolution image reconstruction network structure(PPECA-SRGAN)was proposed.Methods In this paper,a PPECA-SRGAN network based on generative adversarial network(GAN)was proposed.The n etwork model includes a generator and a discriminator.A PA module was added between the residual blocks of the generator to increase the feature extraction capability of OCT retinal image reconstruction.In addition,a PECA module was added to the discriminator,which is an improvement of the pyramid split attention network(PSANet)and can fully capture the spatial information of multi-scale feature maps.First,we used two data sets to test a training set of 1000 images and a test set of 50 images,respectively.The data set was imported into the preprocessing module,and the lowresolution image was obtained through four down-sampling processes.Then,the generator was used to train the model to generate high-resolution images from low-resolution images.When the discriminator could not distinguish the authenticity of the images,it indicated that the generation network generated high-resolution images.Finally,the image quality was evaluated using the structural similarity index measure(SSIM)and PSNR.Results and Discussions The super-resolution index evaluation results of PPECA-SRGAN and the other three models w ere compared,as well as the final reconstruction effect images.In general,PPECA-SRGAN’s reconstruction effect was b etter than SRRes Nt;e however,for the restoration of the image details,the image quality of the PPECA-SRGAN network reconstruction was more in line with the satisfaction degree of human vision.Compared with SRRes Nt,e SRGAN,and ESRGAN,the SSIM indexes of PPECA-SRGAN were 0.090,0.028,and 0.016 higher and the PSNR indexes were 2.15 d B,0.71 d B,and 0.47 d Bhi gher,respectively.The good reconstruction effect of PPECA-SRGAN was due to the addition of the attention mechanism called path aggregation network(PANet)and the proposed attention mechanism named PECA,both enhancing the capture of OCT retinal image features and the reconstruction of details.The PECA module was composed of pyramid splitting and extracting features,with the use of ECANet to fuse multi-scale information.PANet can effectively reduce image noise,such as compression artifacts.This makes our model better than the SRGAN network and other traditional networks.Therefore,the application of the proposed model in OCT image super-resolution reconstruction has been verified,and its performance has been improved compared with other reconstruction algorithms.Conclusions The PPECA-SRGAN network structure proposed in this paper is an improved model of the SRGAN network f or super-resolution reconstruction of retinal OCT B-scan images.We conducted training and verification on MICCAI RETOUCH data set and data collected by Wenzhou Medical University to solve the problems of low-resolution and few details of images collected by OCT.We used advanced GAN to improve the super-resolution reconstruction of OCT images,and the SRGAN network was improved due to the difference in reconstruction between medical images and natural images.Firstly,a PANet module was added between the residual blocks of the generator to extract multi-scale feature relations by pyramid structure and suppress unnecessary artifacts.Then,the PECA module was inserted into the discriminator to effectively combine spatial and channel attentions to learn more image details for the discriminator and obtain richer image pair feature information.The experimental results show that this model is effective and stable in improving the resolution of medical images.Compared with SRRes Nt,e SRGAN,and ESRGAN,the PSNR and SSIM indexes of the reconstructed images were improved by about 3.5%and 5.6%,respectively.In clinical diagnosis,the pro posed algorithm can overcome the inherent limitations of low-resolution imaging systems and reconstruct various details lost in the process of image acquisition;the algorithm is easy to integrate and implement.In the future,if higherquality data sets and lighter algorithms can be obtained,it is possible to further improve the quality of super-resolution reconstruction medical images and make them more applicable in clinical practice.
作者
柯舒婷
陈明惠
郑泽希
袁媛
王腾
何龙喜
吕林杰
孙好
Ke Shuting;Chen Minghui;Zheng Zexi;Yuan Yuan;Wang Teng;He Longxi;Lü Linjie;Sun Hao(Shanghai Engineering Research Center of Interventional Medical Device,the Ministry of Education of Medical Optical Engineering Center,School of Health Sciences and Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;School of Mechanical Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《中国激光》
EI
CAS
CSCD
北大核心
2022年第15期84-92,共9页
Chinese Journal of Lasers
基金
上海市科委产学研医项目(15DZ1940400)。
关键词
生物光学
光学相干层析成像
超分辨率
生成对抗网络
无配对图像
biotechnology
optical coherence tomography
super-resolution
generative adversarial network
unpaired image
