Depth-extended acoustic-resolution photoacoustic microscopy based on a two-stage deep learning network
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Le résumé fourni par la source
Acoustic resolution photoacoustic microscopy (AR-PAM) is a major modality of photoacoustic imaging. It can non-invasively provide high-resolution morphological and functional information about biological tissues. However, the image quality of AR-PAM degrades rapidly when the targets move far away from the focus. Although some works have been conducted to extend the high-resolution imaging depth of AR-PAM, most of them have a small focal point requirement, which is generally not satisfied in a regular AR-PAM system. Therefore, we propose a two-stage deep learning (DL) reconstruction strategy for AR-PAM to recover high-resolution photoacoustic images at different out-of-focus depths adaptively. The residual U-Net with attention gate was developed to implement the image reconstruction. We carried out phantom and in vivo experiments to optimize the proposed DL network and verify the performance of the proposed reconstruction method. Experimental results demonstrated that our approach extends the depth-of-focus of AR-PAM from 1mm to 3mm under the 4 mJ/cm 2 light energy used in the imaging system. In addition, the imaging resolution of the region 2 mm far away from the focus can be improved, similar to the in-focus area. The proposed method effectively improves the imaging ability of AR-PAM and thus could be used in various biomedical studies needing deeper depth.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Depth-extended acoustic-resolution photoacoustic microscopy based on a two-stage deep learning network
- Date Crossref
- 27/07/2022
- Éditeur
- Optica Publishing Group
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Qufu Normal University pays non établi dans la noticeUniversité ou école supérieure
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Chinese Academy of Sciences pays non établi dans la noticeOrganisme public
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Shenzhen Institutes of Advanced Technology pays non établi dans la noticeStructure de recherche
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Zhujiang Hospital pays non établi dans la noticeÉtablissement de santé
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Southern Medical University Department of Hepatobiliary Surgery I pays non établi dans la noticeUniversité ou école supérieure
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Institute of Biomedical and Health Engineering pays non établi dans la noticeStructure de recherche
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These authors contributed equally to this work pays non établi dans la noticeInstitution
Qufu Normal University, Chinese Academy of Sciences et Shenzhen Institutes of Advanced Technology, avec 4 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.