CASIA OpenIR  > 中国科学院分子影像重点实验室
Domain Transform Network for Photoacoustic Tomography from Limited-view and Sparsely Sampled Data
Tong Tong1,5; Wenhui Huang2,3; Kun Wang1,5; Zicong He3; Lin Yin1,5; Xin Yang1,5; Shuixing Zhang3; Jie Tian1,4,5
Source PublicationPhotoacoustics
ISSN2213-5979
2020
Volume19Issue:19Pages:100190
Abstract

Medical image reconstruction methods based on deep learning have recently demonstrated powerful performance
in photoacoustic tomography (PAT) from limited-view and sparse data. However, because most of these
methods must utilize conventional linear reconstruction methods to implement signal-to-image transformations,
their performance is restricted. In this paper, we propose a novel deep learning reconstruction approach that
integrates appropriate data pre-processing and training strategies. The Feature Projection Network (FPnet)
presented herein is designed to learn this signal-to-image transformation through data-driven learning rather
than through direct use of linear reconstruction. To further improve reconstruction results, our method integrates
an image post-processing network (U-net). Experiments show that the proposed method can achieve
high reconstruction quality from limited-view data with sparse measurements. When employing GPU acceleration,
this method can achieve a reconstruction speed of 15 frames per second.

KeywordDeep learning Photoacoustic tomography Domain transformation Medical image reconstruction
DOI10.1016/j.pacs.2020.100190
WOS KeywordRECONSTRUCTION ; PROPAGATION ; ALGORITHM ; MODELS
Indexed BySCI
Language英语
Funding ProjectMinistry of Science and Technology of China[2017YFA0205200] ; Ministry of Science and Technology of China[2017YFA0700401] ; Ministry of Science and Technology of China[2016YFC0103803] ; National Natural Science Foundation of China[61671449] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81527805] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[KFJ-STS-ZDTP-059] ; Chinese Academy of Sciences[YJKYYQ20180048] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Chinese Academy of Sciences[XDBS01030200]
Funding OrganizationMinistry of Science and Technology of China ; National Natural Science Foundation of China ; Chinese Academy of Sciences
WOS Research AreaAcoustics ; Engineering ; Instruments & Instrumentation ; Radiology, Nuclear Medicine & Medical Imaging
WOS SubjectAcoustics ; Engineering, Biomedical ; Instruments & Instrumentation ; Radiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000573284900002
PublisherELSEVIER GMBH
Citation statistics
Cited Times:15[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/41464
Collection中国科学院分子影像重点实验室
Corresponding AuthorTong Tong; Jie Tian
Affiliation1.CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2.College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China
3.Medical Imaging Center, the First Affiliated Hospital, Jinan University, Guangzhou, 510632, China
4.Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100191, China
5.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Tong Tong,Wenhui Huang,Kun Wang,et al. Domain Transform Network for Photoacoustic Tomography from Limited-view and Sparsely Sampled Data[J]. Photoacoustics,2020,19(19):100190.
APA Tong Tong.,Wenhui Huang.,Kun Wang.,Zicong He.,Lin Yin.,...&Jie Tian.(2020).Domain Transform Network for Photoacoustic Tomography from Limited-view and Sparsely Sampled Data.Photoacoustics,19(19),100190.
MLA Tong Tong,et al."Domain Transform Network for Photoacoustic Tomography from Limited-view and Sparsely Sampled Data".Photoacoustics 19.19(2020):100190.
Files in This Item: Download All
File Name/Size DocType Version Access License
光声断层成像智能重建算法.pdf(9199KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Tong Tong]'s Articles
[Wenhui Huang]'s Articles
[Kun Wang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Tong Tong]'s Articles
[Wenhui Huang]'s Articles
[Kun Wang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Tong Tong]'s Articles
[Wenhui Huang]'s Articles
[Kun Wang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: 光声断层成像智能重建算法.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.