基于改进的U-Net神经网络的稀疏视角光声图像质量增强方法  

Sparse-View Photoacoustic Image Quality Enhancement Based on a Modified U-Net

作　　者：王通 董文德 沈康 刘松德 刘文[1] 田超[3,4] Wang Tong;Dong Wende;Shen Kang;Liu Songde;Liu Wen;Tian Chao(School of Physical Science,University of Science and Technology of China,Hefei,Anhui 230026,China;College of Automation Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing,Jiangsu 211106,China;School of Engineering Science,University of Science and Technology of China,Hefei,Anhui 230026,China;Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes,Hefei,Anhui 230026,China)

机构地区：[1]中国科学技术大学物理学院,安徽合肥230026 [2]南京航空航天大学自动化学院,江苏南京211106 [3]中国科学技术大学工程学院,安徽合肥230026 [4]精密科学仪器安徽普通高校重点实验室,安徽合肥230026

出　　处：《激光与光电子学进展》2022年第6期294-303,共10页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(62122072,12174368,61705216,61905112);安徽省科技重大专项(18030801138);之江实验室(2019MC0AB01);中国科学院人才项目;统筹推进世界一流大学和一流学科建设专项资金。

摘　　要：在光声断层成像中,通常利用超声换能器阵列接收光声信号,其制造成本较高,并且阵元数量对最终成像质量有重要影响。为了提升稀疏视角下光声重建的图像质量,提出了一种基于改进的U-Net神经网络结构的稀疏视角光声图像质量增强方法,该方法采用的改进的U-Net网络的特点在于通过添加连续卷积层替换跳接层,提升编码器和解码器拼接特征的匹配度;同时利用了基于多尺度结构相似性指数的损失函数对网络进行训练。基于仿体数据集和活体数据集的实验结果表明,改进的U-Net网络具有很好的图像细节重建能力,其所得的重建图像质量优于经典的U-Net网络。In photoacoustic tomography,an ultrasonic transducer array is usually used to receive photoacoustic signals,which is expensive to manufacture,and the number of array elements has an important impact on the final imaging quality.To improve photoacoustic image quality reconstructed under sparse view conditoin,this study proposes a modified U-Net based on the replacement of the skip connection in a conventional U-Net with continuous convolutional layers,thereby increasing the matching degree of features transferred from the encoder to the decoder.Furthermore,the loss function based on the structural similarity index measure is used to train the network.Experimental results based on simulation and in vivo dataset show that compared with the conventional U-Net,the modified U-Net achieves more image details and the quality of the reconstructed image is significantly better.

关 键 词：医用光学与生物光学 光声断层成像 图像重建 稀疏视角 深度学习 U-Net 

分 类 号：R318.51[医药卫生—生物医学工程]