Quantitative photoacoustic blood oxygenation imaging using deep residual and recurrent neural network

doi:10.1109/ISBI.2019.8759438

	Quantitative photoacoustic blood oxygenation imaging using deep residual and recurrent neural network
	Changchun Yang; Hengrong Lan; Hongtao Zhong; Fei Gao
	2019-04-01
会议录名称	PROCEEDINGS - INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING
ISSN	1945-7928
卷号	2019-April
页码	741-744
发表状态	已发表
DOI	10.1109/ISBI.2019.8759438
摘要	Diffusive light scattering in biological tissue causes a heterogeneous and variable spectrum of light fluence, which is a significant challenge to predict due to unknown optical properties of tissues. Then it is difficult to accurately and quantitatively image blood oxygen saturation (sO) in photoacoustic imaging because of unknown fluence distribution. To tackle this problem, we develop a deep residual, and recurrent neural network, i.e., DR2U-net for the quantitative estimation of blood oxygenation of photoacoustic imaging. The fine-tuned DR2U-net we developed can extract fluence distribution information from optical absorption images only using two wavelengths of light in Monte Carlo simulation, and afterward generate a quantitative image of sO. The measurement results of sO show a very high accuracy by testing in simulated biological tissue, and its error is as low as 1.27% compared with conventional linear mixing method (48.76%). Besides, our model is high-speed, about 18. 4ms is achieved for every quantitative sO2 image. © 2019 IEEE.
会议录编者/会议主办者	et al. ; IEEE Engineering in Medicine and Biology Society (EMB) ; IEEE Signal Processing Society ; The Institute of Electrical and Electronics Engineers (IEEE) ; UAI ; United Imaging Intelligence
关键词	Blood Light scattering Intelligent systems Deep neural networks Photoacoustic effect Tissue Optical properties Monte Carlo methods Blood oxygen saturation Diffusive light scattering Fluence distribution Optical absorption images Photo-acoustic imaging Quantitative estimation Quantitative images Residual recurrent u-net
会议名称	16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
会议地点	Venice, Italy
会议日期	April 8, 2019 - April 11, 2019
URL	查看原文
收录类别	EI
语种	英语
出版者	IEEE Computer Society
EI入藏号	20193207270353
EI主题词	Recurrent neural networks
EISSN	1945-8452
EI分类号	461.2 Biological Materials and Tissue Engineering ; 461.4 Ergonomics and Human Factors Engineering ; 723.4 Artificial Intelligence ; 741.1 Light/Optics ; 751.1 Acoustic Waves ; 922.2 Mathematical Statistics
原始文献类型	Conference article (CA)
来源库	IEEE
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/251831
专题	信息科学与技术学院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_硕士生信息科学与技术学院_博士生
作者单位	Hybrid Imaging System Laboratory, School of Information Science and Technology, ShanghaiTech University, Shanghai, 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	Changchun Yang,Hengrong Lan,Hongtao Zhong,et al. Quantitative photoacoustic blood oxygenation imaging using deep residual and recurrent neural network[C]//et al., IEEE Engineering in Medicine and Biology Society (EMB), IEEE Signal Processing Society, The Institute of Electrical and Electronics Engineers (IEEE), UAI, United Imaging Intelligence:IEEE Computer Society,2019:741-744.