Limited-view photoacoustic imaging reconstruction via high-quality self-supervised neural representation

doi:10.1016/j.pacs.2025.100685

	Limited-view photoacoustic imaging reconstruction via high-quality self-supervised neural representation
	Xiao, Youshen1 ; Shen, Yuting1 ; Liao, Sheng1 ; Yao, Bowei1 ; Cai, Xiran1 ; Zhang, Yuyao1 ; Gao, Fei 2,3,4
	2025-04
发表期刊	PHOTOACOUSTICS (IF:7.1[JCR-2023],7.2[5-Year])
ISSN	2213-5979
卷号	42
DOI	10.1016/j.pacs.2025.100685
摘要	In practical applications within the human body, it is often challenging to fully encompass the target tissue or organ, necessitating the use of limited-view arrays, which can lead to the loss of crucial information. Addressing the reconstruction of photoacoustic sensor signals in limited-view detection spaces has become a focal point of current research. In this study, we introduce a self-supervised network termed HIgh-quality Self-supervised neural representation (HIS), which tackles the inverse problem of photoacoustic imaging to reconstruct high-quality photoacoustic images from sensor data acquired under limited viewpoints. We regard the desired reconstructed photoacoustic image as an implicit continuous function in 2D image space, viewing the pixels of the image as sparse discrete samples. The HIS's objective is to learn the continuous function from limited observations by utilizing a fully connected neural network combined with Fourier feature position encoding. By simply minimizing the error between the network's predicted sensor data and the actual sensor data, HIS is trained to represent the observed continuous model. The results indicate that the proposed HIS model offers superior image reconstruction quality compared to three commonly used methods for photoacoustic image reconstruction. © 2025 The Authors
关键词	Inverse problems Continuous functions High quality Images reconstruction Implicit neural representation Limited-view Neural representations Photo-acoustic imaging Photoacoustic image Self-supervised Sensors data
URL	查看原文
收录类别	EI
语种	英语
出版者	Elsevier GmbH
EI入藏号	20250417765616
EI主题词	Image reconstruction
EI分类号	1106.3.1 ; 1201
原始文献类型	Journal article (JA)
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/483845
专题	信息科学与技术学院信息科学与技术学院_硕士生信息科学与技术学院_PI研究组_张玉瑶组信息科学与技术学院_PI研究组_蔡夕然组
通讯作者	Zhang, Yuyao
作者单位	1.School of Information Science and Technology, ShanghaiTech University, No. 393 HuaXia Middle Road, Pudong New Dist.; 201210, China; 2.School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui; 230026, China; 3.Hybrid Imaging System Laboratory, Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu; 215123, China; 4.School of Engineering Science, University of Science and Technology of China, Hefei, Anhui; 230026, China
第一作者单位	信息科学与技术学院
通讯作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Xiao, Youshen,Shen, Yuting,Liao, Sheng,et al. Limited-view photoacoustic imaging reconstruction via high-quality self-supervised neural representation[J]. PHOTOACOUSTICS,2025,42.
APA	Xiao, Youshen.,Shen, Yuting.,Liao, Sheng.,Yao, Bowei.,Cai, Xiran.,...&Gao, Fei.(2025).Limited-view photoacoustic imaging reconstruction via high-quality self-supervised neural representation.PHOTOACOUSTICS,42.
MLA	Xiao, Youshen,et al."Limited-view photoacoustic imaging reconstruction via high-quality self-supervised neural representation".PHOTOACOUSTICS 42(2025).