基于残差混合域注意力网络的PET超分辨率重建方法

PET super-resolution reconstruction method based on residual mixed domain attention network

正电子发射断层扫描(PET)设备的成像结果常受到扫描时间、示踪剂剂量等因素的制约,导致图像质量下降,影响医生的诊断结果。目前借助人工智能(AI)技术提升PET成像质量是研究的热点,针对现有方法训练参数多,浅层信息丢失,纹理细节损失等问题,提出了一种基于残差混合域注意力网络的PET超分辨率重建方法。该方法设计了一个轻量级的卷积网络,在其中加入残差学习结构并融入混合域注意力块,在增强神经网络的交互性的同时,提高了对高频信息区域的关注度,能够快速重建图像的高频细节。数据集包括网络中的开源数据和从医院获取的临床数据,由此建立PET图像超分辨率数据集,进行训练和测试。实验结果表明,该算法与对比网络在测试结果上有明显提升,当比例因子为4时,与CARN相比,PSNR和SSIM的平均值分别提高了0.09 dB和0.0009,此外参数数量减少了50.26%,有效提升了模型的重建效率。

The imaging results of positron emission tomography(PET)equipment are often constrained by some factors such as tracer dose and scanning time,resulting in the image quality decline and affecting doctors’diagnostic results.At present,improving the quality of PET imaging with artificial intelligence(AI)technology is a hot research topic.Aiming at the problems of existing methods such as many training parameters,loss of shallow information,loss of texture details,etc.,and proposes a method based on residual hybrid domain attention.The PET super-resolution reconstruction method of force network.This method designs a lightweight convolutional network,in which the residual learning structure is added and the mixed domain attention block is incorporated.While enhancing the interaction of the neural network,it also increases the attention to the high-frequency information area then quickly reconstruct the high-frequency details of the image.The data set includes open source data in the network and clinical data obtained from hospitals.As a result,a super-resolution data set of PET images is established for training and testing.The experimental results show that the test results of the algorithm and the comparison network are significantly improved.When the scale factor is 4,compared with CARN,the average values of PSNR and SSIM are increased by 0.09 dB and 0.0009,respectively.In addition,the number of parameters is reduced by 50.26%,which effectively improves the reconstruction efficiency of the model.