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...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/146548 |
| _version_ | 1826120750980923392 |
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| author | Sharma, Arunima Pramanik, Manojit |
| author2 | School of Chemical and Biomedical Engineering |
| author_facet | School of Chemical and Biomedical Engineering Sharma, Arunima Pramanik, Manojit |
| author_sort | Sharma, Arunima |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T05:21:52Z |
| format | Journal Article |
| id | ntu-10356/146548 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:21:52Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-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 |
| 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 |
| title | 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_short | 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 |
| topic | Engineering::Bioengineering Acoustic Resolution Photoacoustic Microscopy (AR-PAM) Ultrasound Transducer (UST) |
| url | https://hdl.handle.net/10356/146548 |
| work_keys_str_mv | AT sharmaarunima convolutionalneuralnetworkforresolutionenhancementandnoisereductioninacousticresolutionphotoacousticmicroscopy AT pramanikmanojit convolutionalneuralnetworkforresolutionenhancementandnoisereductioninacousticresolutionphotoacousticmicroscopy |