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Dictionary learning sparse-sampling reconstruction method for in-vivo 3D photoacoustic computed tomography

Liu, Fangyan and Gong, Xiaojing and Wang, Lihong V. and Guan, Jingjing and Song, Liang and Meng, Jing (2019) Dictionary learning sparse-sampling reconstruction method for in-vivo 3D photoacoustic computed tomography. Biomedical Optics Express, 10 (4). pp. 1660-1677. ISSN 2156-7085. PMCID PMC6484974. https://resolver.caltech.edu/CaltechAUTHORS:20190412-102929853

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Abstract

The sparse transforms currently used in the model-based reconstruction method for photoacoustic computed tomography (PACT) are predefined and they typically cannot capture the underlying features of the specific data sets adequately, thus limiting the high-quality recovery of photoacoustic images. In this work, we present an advanced reconstruction model using the K-VSD dictionary learning technique and present the in vivo results after adapting the model into the 3D PACT system. The in vivo experiments were performed on an IRB approved human hand and two rats. When compared to the traditional sparse transform, experimental results using our proposed method improved accuracy and contrast to noise ration of the reconstructed photoacoustic images, on average, by 3.7 and 1.8 times in the case of 50% sparse-sampling rate, respectively. We also compared the performance of our algorithm against other techniques, and imaging speed was 60% faster than other approaches. Our system would require sparse-transducer array and lower number of data acquisition hardware (DAQs) potentially reducing the cost of the system. Thus, our work provides a new way for reconstructing photoacoustic images, and it would enable the development of new high-speed low-cost 3D PACT for various biomedical applications.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1364/boe.10.001660DOIArticle
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6484974PubMed CentralArticle
ORCID:
AuthorORCID
Wang, Lihong V.0000-0001-9783-4383
Meng, Jing0000-0002-9069-8988
Additional Information:© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement. Received 10 Dec 2018; revised 14 Feb 2019; accepted 17 Feb 2019; published 5 Mar 2019. Funding: National Natural Science Foundation of China (NSFC) (81522024, 81427804, 61475182, and 61308116); Shenzhen Science and Technology Innovation grant (JCYJ20160608214524052). We would like to thank C. B. Liu and Y. Y. Bai for beneficial discussions on the reconstruction algorithms. The authors declare that there are no conflicts of interest related to this article.
Funders:
Funding AgencyGrant Number
National Natural Science Foundation of China81522024
National Natural Science Foundation of China81427804
National Natural Science Foundation of China61475182
National Natural Science Foundation of China61308116
Shenzhen Science and Technology Innovation GrantJCYJ20160608214524052
Issue or Number:4
PubMed Central ID:PMC6484974
Record Number:CaltechAUTHORS:20190412-102929853
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190412-102929853
Official Citation:Fangyan Liu, Xiaojing Gong, Lihong V. Wang, Jingjing Guan, Liang Song, and Jing Meng, "Dictionary learning sparse-sampling reconstruction method for in-vivo 3D photoacoustic computed tomography," Biomed. Opt. Express 10, 1660-1677 (2019)
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94690
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:12 Apr 2019 22:22
Last Modified:03 Oct 2019 21:06

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