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End-to-end Res-Unet based reconstruction algorithm for photoacoustic imaging
Biomedical Optics Express ( IF 3.4 ) Pub Date : 2020-08-27 , DOI: 10.1364/boe.396598
Jinchao Feng , Jianguang Deng , Zhe Li , Zhonghua Sun , Huijing Dou , Kebin Jia

Recently, deep neural networks have attracted great attention in photoacoustic imaging (PAI). In PAI, reconstructing the initial pressure distribution from acquired photoacoustic (PA) signals is a typically inverse problem. In this paper, an end-to-end Unet with residual blocks (Res-Unet) is designed and trained to solve the inverse problem in PAI. The performance of the proposed algorithm is explored and analyzed by comparing a recent model-resolution-based regularization algorithm (MRR) with numerical and physical phantom experiments. The improvement obtained in the reconstructed images was more than 95% in pearson correlation and 39% in peak signal-to-noise ratio in comparison to the MRR. The Res-Unet also achieved superior performance over the state-of-the-art Unet++ architecture by more than 18% in PSNR in simulation experiments.

中文翻译:

基于端到端Res-Unet的光声成像重建算法

最近,深层神经网络在光声成像(PAI)中引起了极大的关注。在PAI中,从采集的光声(PA)信号重建初始压力分布通常是一个反问题。本文设计并训练了带有残差块的端到端Unet(Res-Unet),以解决PAI中的逆问题。通过将最新的基于模型分辨率的正则化算法(MRR)与数字和物理幻象实验进行比较,来探索和分析所提出算法的性能。与MRR相比,重建图像中的皮尔逊相关性改善了95%以上,峰值信噪比改善了39%。在仿真实验中,Res-Unet在PSNR方面也比最新的Unet ++架构高出18%。
更新日期:2020-09-01
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