Deep learning boosts the imaging speed of photoacoustic endomicroscopy

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High-speed photoacoustic (PA) endomicroscopy imaging is desired for real-time guidance of minimally invasive surgery. However, the imaging speed of wavefront shaping-based endomicroscopy has been limited by the speed of spatial light modulators. In this work, a deep convolutional neural network was used to improve the imaging speed of a newly developed PA endomicroscopy system by enhancing sparsely sampled PA images. With a carbon fibre phantom, this method increased the imaging speed by 16 times without significantly affecting the image quality. With further validation on more complex datasets, this approach is promising to achieve real-time PA endomicroscopy imaging via wavefront shaping.
Original languageEnglish
Title of host publicationProc. SPIE 12379
Subtitle of host publicationPhotons Plus Ultrasound: Imaging and Sensing 2023
EditorsAlexander A. Oraevsky, Lihong V. Wang
Number of pages5
ISBN (Electronic)9781510658639
Publication statusPublished - 9 Mar 2023

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


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