Convolutional neural network for resolution enhancement and noise reduction in acoustic resolution photoacoustic microscopy
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 reso...
Saved in:
Main Authors: | , |
---|---|
Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2021
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/146548 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-146548 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1465482023-12-29T06:49:05Z Convolutional neural network for resolution enhancement and noise reduction in acoustic resolution photoacoustic microscopy Sharma, Arunima Pramanik, Manojit School of Chemical and Biomedical Engineering Engineering::Bioengineering Acoustic Resolution Photoacoustic Microscopy (AR-PAM) Ultrasound Transducer (UST) 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. Ministry of Education (MOE) Published version Ministry of Education - Singapore (RG144/18). 2021-02-26T00:43:34Z 2021-02-26T00:43:34Z 2020 Journal Article 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 2156-7085 https://hdl.handle.net/10356/146548 10.1364/BOE.411257 33408964 2-s2.0-85096861842 12 11 6826 6839 en Biomedical Optics Express © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement. application/pdf |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
Engineering::Bioengineering Acoustic Resolution Photoacoustic Microscopy (AR-PAM) Ultrasound Transducer (UST) |
spellingShingle |
Engineering::Bioengineering Acoustic Resolution Photoacoustic Microscopy (AR-PAM) Ultrasound Transducer (UST) Sharma, Arunima Pramanik, Manojit Convolutional neural network for resolution enhancement and noise reduction in acoustic resolution photoacoustic microscopy |
description |
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. |
author2 |
School of Chemical and Biomedical Engineering |
author_facet |
School of Chemical and Biomedical Engineering Sharma, Arunima Pramanik, Manojit |
format |
Article |
author |
Sharma, Arunima Pramanik, Manojit |
author_sort |
Sharma, Arunima |
title |
Convolutional neural network for resolution enhancement and noise reduction in acoustic resolution photoacoustic microscopy |
title_short |
Convolutional neural network for resolution enhancement and noise reduction in acoustic resolution photoacoustic microscopy |
title_full |
Convolutional neural network for resolution enhancement and noise reduction in acoustic resolution photoacoustic microscopy |
title_fullStr |
Convolutional neural network for resolution enhancement and noise reduction in acoustic resolution photoacoustic microscopy |
title_full_unstemmed |
Convolutional neural network for resolution enhancement and noise reduction in acoustic resolution photoacoustic microscopy |
title_sort |
convolutional neural network for resolution enhancement and noise reduction in acoustic resolution photoacoustic microscopy |
publishDate |
2021 |
url |
https://hdl.handle.net/10356/146548 |
_version_ |
1787136611006283776 |