Hybrid Neural Network for Photoacoustic Imaging Reconstruction

doi:10.1109/EMBC.2019.8857019

	Hybrid Neural Network for Photoacoustic Imaging Reconstruction
	Hengrong Lan1 ; Kang Zhou1 ; Changchun Yang1 ; Jiang Liu 2; Shenghua Gao1 ; Fei Gao1
	2019-07
会议录名称	2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
ISSN	1557-170X
页码	6367-6370
发表状态	已发表
DOI	10.1109/EMBC.2019.8857019
摘要	Photoacoustic imaging (PAI) is an emerging noninvasive imaging modality combining the advantages of ultrasound imaging and optical imaging. Image reconstruction is an essential topic in photoacoustic imaging, which is unfortunately an ill-posed problem due to the complex and unknown optical/acoustic parameters in tissue. Conventional algorithms used in photoacoustic imaging (e.g., delay-and-sum) provide a fast solution while many artifacts remain. Convolutional neural network (CNN) has shown state-of-the-art results in computer vision, and more and more work based on CNN has been studied in medical image processing recently. In this paper, we propose Y-Net: a CNN architecture to reconstruct the PA image by integrating both raw data and beamformed images as input. The network connected two encoders with one decoder path, which optimally utilizes more information from raw data and beamformed image. The results of the simulation showed a good performance compared with conventional deep-learning based algorithms and other model-based methods. The proposed Y-Net architecture has significant potential in medical image reconstruction beyond PAI.
关键词	Decoding Image reconstruction Array signal processing Photoacoustic imaging Training Optical imaging
会议地点	Berlin, Germany
会议日期	23-27 July 2019
URL	查看原文
收录类别	EI ; CPCI-S ; CPCI
EI入藏号	20200308034559
原始文献类型	Conference article (CA)
引用统计
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/102105
专题	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_高飞组信息科学与技术学院_PI研究组_高盛华组信息科学与技术学院_博士生
作者单位	1.School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China 2.Ningbo Institute of Material Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
第一作者单位	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	Hengrong Lan,Kang Zhou,Changchun Yang,et al. Hybrid Neural Network for Photoacoustic Imaging Reconstruction[C],2019:6367-6370.