Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146548
Title: Convolutional neural network for resolution enhancement and noise reduction in acoustic resolution photoacoustic microscopy
Authors: Sharma, Arunima
Pramanik, Manojit
Keywords: Engineering::Bioengineering
Issue Date: 2020
Source: Sharma, A., & Pramanik, M. (2020). Convolutional neural network for resolution enhancement and noise reduction in acoustic resolution photoacoustic microscopy. Biomedical Optics Express, 11(12), 6826-6839. doi:10.1364/BOE.411257
Journal: Biomedical Optics Express
Abstract: 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 in vivo rat vasculature imaging further validated that noise-free, high resolution images can be obtained using this method.
URI: https://hdl.handle.net/10356/146548
ISSN: 2156-7085
DOI: 10.1364/BOE.411257
Rights: © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCBE Journal Articles

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