Open AccessEnd-to-end Res-Unet based reconstruction algorithm for photoacoustic imaging
Author(s) -
Jinchao Feng,
Jianguang Deng,
Zhe Li,
Zhonghua Sun,
Haoran Dou,
Kebin Jia
Publication year - 2020
Publication title -
biomedical optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.396598
Subject(s) - algorithm , computer science , photoacoustic imaging in biomedicine , inverse problem , residual , imaging phantom , iterative reconstruction , regularization (linguistics) , artificial intelligence , mathematics , optics , physics , mathematical analysis
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.