Super-resolution photoacoustic microscopy based on deep learning

Zhuangzhuang Wang; Sihang Li; Xianlin Song

doi:10.1117/12.2589655

12 April 2021 Super-resolution photoacoustic microscopy based on deep learning

Zhuangzhuang Wang, Sihang Li, Xianlin Song

Proceedings Volume 11736, Real-Time Image Processing and Deep Learning 2021; 1173608 (2021) https://doi.org/10.1117/12.2589655
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Photoacoustic imaging is an emerging imaging technology based on the photoacoustic effect. As a hybrid imaging technology that combines pure optical imaging and ultrasound imaging, it also has the advantages of optical imaging with high resolution and rich contrast. And the advantage of high penetration depth of acoustic imaging. With its advantages, photoacoustic imaging has extremely broad applications in biomedical testing, such as brain imaging and tumor imaging. Due to the optical diffraction limit of the objective lens, the image resolution of the obtained image is hard to be further improved, therefore, finer structural information is difficult to obtain. In order to solve this problem, we use an end-to-end convolutional neural network from low resolution to high resolution to further process the obtained low-resolution images to obtain optimized high-resolution image and improve the quality of imaging. A convolutional neural network is built on the pycharm platform through the open source Tensorflow library. Bicubic interpolation is used to preprocess the original data. Then we perform network training on the processed sample data and finally a series of photoacoustic microscopy images of cerebral blood vessels^[1,2] were tested. The test results show that the resolution of the image is significantly improved, and a clearer image is obtained. The experimental results verify that this end-to-end convolutional neural network from low resolution to high resolution can effectively improve the resolution of photoacoustic imaging. This has laid a good foundation for the follow-up biomedical research^[3] of photoacoustic imaging technology.

Conference Presentation

Citation Download Citation

Zhuangzhuang Wang, Sihang Li, and Xianlin Song "Super-resolution photoacoustic microscopy based on deep learning", Proc. SPIE 11736, Real-Time Image Processing and Deep Learning 2021, 1173608 (12 April 2021); https://doi.org/10.1117/12.2589655

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available