Convolutional neural network for resolution enhancement and noise reduction in acoustic resolution photoacoustic microscopy.
Journal
Biomedical optics express
ISSN: 2156-7085
Titre abrégé: Biomed Opt Express
Pays: United States
ID NLM: 101540630
Informations de publication
Date de publication:
01 Dec 2020
01 Dec 2020
Historique:
received:
29
09
2020
revised:
24
10
2020
accepted:
24
10
2020
entrez:
7
1
2021
pubmed:
8
1
2021
medline:
8
1
2021
Statut:
epublish
Résumé
In acoustic resolution photoacoustic microscopy (AR-PAM), a high numerical aperture focused ultrasound transducer (UST) is used for deep tissue high resolution photoacoustic imaging. There is a significant degradation of lateral resolution in the out-of-focus region. Improvement in out-of-focus resolution without degrading the image quality remains a challenge. In this work, we propose a deep learning-based method to improve the resolution of AR-PAM images, especially at the out of focus plane. A modified fully dense U-Net based architecture was trained on simulated AR-PAM images. Applying the trained model on experimental images showed that the variation in resolution is ∼10% across the entire imaging depth (∼4 mm) in the deep learning-based method, compared to ∼180% variation in the original PAM images. Performance of the trained network on
Identifiants
pubmed: 33408964
doi: 10.1364/BOE.411257
pii: 411257
pmc: PMC7747888
doi:
Types de publication
Journal Article
Langues
eng
Pagination
6826-6839Informations de copyright
© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.
Déclaration de conflit d'intérêts
The authors declare that there are no conflicts of interest related to this article.
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