基于双域神经网络的稀疏视角光声图像重建  

Dual-Domain Neural Network for Sparse-View Photoacoustic Image Reconstruction

作　　者：沈康 刘松德 施钧辉 田超 Shen Kang;Liu Songde;Shi Junhui;Tian Chao(School of Engineering Science,University of Science and Technology of Chiyia,Hefei,Ajihui 230026,China;Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes,Hefei,Anhui 230026,China;Zhejiang Lab,Hangzhou,Zhejiang 311121,China)

机构地区：[1]中国科学技术大学工程科学学院,安徽合肥230026 [2]精密科学仪器安徽普通高校重点实验室,安徽合肥230026 [3]之江实验室,浙江杭州311121

出　　处：《中国激光》2022年第5期167-179,共13页Chinese Journal of Lasers

基　　金：国家自然科学基金(62122072,12174368,61705216);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：光声计算断层成像(PACT)是近年来迅速发展的一种无损生物医学成像技术,在生物医学领域有着较高的应用价值。为了获得高质量的光声图像,成像系统的信号采集装置需要配备高密度的阵列探测器。但在实际应用中,由于经济成本、制造工艺及成像时间等因素的限制,探测器的排布往往较为稀疏,难以实现稳定重建,导致重建图像中出现条纹伪影。为了解决这一问题,本文提出一种基于双域神经网络的PACT图像重建算法。该算法主要包含三个模块:数据域网络、反投影层和图像域网络,其中数据域网络和图像域网络可分别对光声数据和光声图像进行增强,以提升图像质量。为了对网络进行训练和测试,构建了一个血管仿真数据集和一个小鼠活体试验数据集。研究结果表明,所提算法可以有效地抑制条纹伪影,提升图像质量,并且重建性能优于其他重建算法。Objective Photoacoustic computed tomography(PACT)is a fast-evolving noninvasive biomedical imaging technique that shows great potential for basic life sciences and clinical practice.To generate high-quality photoacoustic(PA)images,imaging systems need to employ a dense array of ultrasonic detectors.However,due to economic constraints,fabrication complexity,and real-time data processing requirements,ultrasonic detectors are usually arranged sparsely.Such sparsity cannot satisfy the essential conditions of stable image reconstruction and results in significant artifacts in reconstructed images.To address this issue,we develop an innovative PACT image reconstruction algorithm based on a dual-domain neural network.Methods The proposed network(Fig.1),which we refer to as DI-Net,consists of a data-domain network(D-Net),a back projection layer and an image-domain network(I-Net).Both D-Net and I-Net are designed based on U-Net,a convolutional neural network that is developed for biomedical image segmentation.Based on U-Net,an instance normalization,a skip connection,and a leaky rectified linear unit are used to enhance the performance of the DI-Net.The back projection layer is a sparse matrix with fixed parameters that allows for gradient propagation from I-Net to D-Net.First,the D-Net maps sparse-view PA data into dense-view PA data in the data domain.Then,the back projection layer transforms the dense-view PA data into a PA image.Finally,the reconstructed image is further enhanced in the image domain by the I-Net.The performance of DI-Net is evaluated through numerical simulations and in vivo experimental data that contains 128-views and 256-views undersampled data.In addition,to further demonstrate the effectiveness of the network,two popular algorithms,i.e.,filtered back projection(FBP)and Post-Unet,are compared with the proposed DI-Net.Results and Discussions We first numerically test the performance of DI-Net using a synthetic vascular phantom dataset.The reconstruction results of 128 views show that the image reco

关 键 词：生物光学 光声成像 图像重建 神经网络 稀疏视角 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]