A Self-supervised Learning Reconstruction Algorithm with an Encoder-Decoder Architecture for Diffuse Optical Tomography - Details

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Abstract：

Diffuse　optical　tomography　(DOT)　is　an　emerging　non-invasive　optical　imaging　technique,　which　has　a　promising　application　in　breast　cancer　detection　and　diagnosis.　However,　the　conventional　image　reconstruction　algorithm　in　DOT　is　time-consuming　and　easy　to　error　when　recovering　the　distribution　of　optical　parameters　within　the　complete　tissue.　In　this　paper,　we　present　an　end-to-end　reconstruction　algorithm　for　DOT　based　on　a　deep　convolutional　encoder-decoder　architecture,　which　consists　of　a　data　processing　part　and　a　convolutional　encoder-decoder　net.　Its　effectiveness　was　evaluated　using　simulation　data.　The　results　show　that　the　overall　quality　of　our　method　is　significantly　improved　compared　with　the　traditional　algorithm　based　on　the　FEM　method,　the　single　inclusion　deviation　is　reduced　by　150%　compared　with　the　traditional　algorithm,　the　standard　deviation　is　reduced　by　50%;　multiple　inclusions　deviation　is　reduced　by　100%　and　the　standard　deviation　by　38.7%.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.

Keyword：

Learning systems Image reconstruction Medical imaging Deep neural networks Statistics Signal encoding Decoding Convolution Convolutional neural networks Data handling Supervised learning Optical tomography Network architecture

Author Community：

[ 1 ] [Li, Yaxuan]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 2 ] [Li, Yaxuan]Beijing Laboratory of Advanced Information Networks, Beijing; 100124, China
[ 3 ] [Wei, Chengpu]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 4 ] [Wei, Chengpu]Beijing Laboratory of Advanced Information Networks, Beijing; 100124, China
[ 5 ] [Zhang, Wenqian]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 6 ] [Zhang, Wenqian]Beijing Laboratory of Advanced Information Networks, Beijing; 100124, China
[ 7 ] [Li, Zhe]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 8 ] [Li, Zhe]Beijing Laboratory of Advanced Information Networks, Beijing; 100124, China
[ 9 ] [Sun, Zhonghua]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 10 ] [Sun, Zhonghua]Beijing Laboratory of Advanced Information Networks, Beijing; 100124, China
[ 11 ] [Jia, Kebin]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 12 ] [Jia, Kebin]Beijing Laboratory of Advanced Information Networks, Beijing; 100124, China
[ 13 ] [Feng, Jinchao]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 14 ] [Feng, Jinchao]Beijing Laboratory of Advanced Information Networks, Beijing; 100124, China

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Source ：

ISSN： 1865-0929

Year： 2023

Volume： 1910 CCIS

Page： 19-29

Language： English

Cited Count：

WoS CC Cited Count： 0

SCOPUS Cited Count： 1

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 8

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