Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/94275
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Biomedical Engineering | en_US |
dc.contributor | Mainland Development Office | en_US |
dc.creator | Cheng, S | en_US |
dc.creator | Zhou, Y | en_US |
dc.creator | Chen, J | en_US |
dc.creator | Li, H | en_US |
dc.creator | Wang, L | en_US |
dc.creator | Lai, P | en_US |
dc.date.accessioned | 2022-08-11T02:01:34Z | - |
dc.date.available | 2022-08-11T02:01:34Z | - |
dc.identifier.issn | 2213-5987 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/94275 | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | © 2021 The Authors. Published by Elsevier GmbH. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). | en_US |
dc.rights | The following publication Cheng, S., Zhou, Y., Chen, J., Li, H., Wang, L., & Lai, P. (2022). High-resolution photoacoustic microscopy with deep penetration through learning. Photoacoustics, 25, 100314 is available at https://doi.org/10.1016/j.pacs.2021.100314 | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Deep penetration | en_US |
dc.subject | Photoacoustic microscopy | en_US |
dc.title | High-resolution photoacoustic microscopy with deep penetration through learning | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 25 | en_US |
dc.identifier.doi | 10.1016/j.pacs.2021.100314 | en_US |
dcterms.abstract | Optical-resolution photoacoustic microscopy (OR-PAM) enjoys superior spatial resolution and has received intense attention in recent years. The application, however, has been limited to shallow depths because of strong scattering of light in biological tissues. In this work, we propose to achieve deep-penetrating OR-PAM performance by using deep learning enabled image transformation on blurry living mouse vascular images that were acquired with an acoustic-resolution photoacoustic microscopy (AR-PAM) setup. A generative adversarial network (GAN) was trained in this study and improved the imaging lateral resolution of AR-PAM from 54.0 µm to 5.1 µm, comparable to that of a typical OR-PAM (4.7 µm). The feasibility of the network was evaluated with living mouse ear data, producing superior microvasculature images that outperforms blind deconvolution. The generalization of the network was validated with in vivo mouse brain data. Moreover, it was shown experimentally that the deep-learning method can retain high resolution at tissue depths beyond one optical transport mean free path. Whilst it can be further improved, the proposed method provides new horizons to expand the scope of OR-PAM towards deep-tissue imaging and wide applications in biomedicine. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Photoacoustics, Mar. 2022, v. 25, 100314 | en_US |
dcterms.isPartOf | Photoacoustics | en_US |
dcterms.issued | 2022-03 | - |
dc.identifier.scopus | 2-s2.0-85118876283 | - |
dc.identifier.eissn | 2213-5979 | en_US |
dc.identifier.artn | 100314 | en_US |
dc.description.validate | 202208 bckw | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | a1564 | - |
dc.identifier.SubFormID | 45437 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China; Guangdong Science and Technology Commission; Hong Kong Innovation and Technology Commission; Shenzhen Science and Technology Innovation Commission | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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1-s2.0-S2213597921000732-main.pdf | 12.95 MB | Adobe PDF | View/Open |
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