Deep neural network-based bandwidth enhancement of photoacoustic data

Photoacoustic (PA) signals collected at the boundary of tissue are always band-limited. A deep neural network (DNN) was proposed to enhance the bandwidth of the detected PA signal, thereby improving the quantitative accuracy of the reconstructed PA images. A least square based deconvolution method t...

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Main Authors: Gutta, Sreedevi, Kadimesetty, Venkata Suryanarayana, Kalva, Sandeep Kumar, Pramanik, Manojit, Ganapathy, Sriram, Yalavarthy, Phaneendra K.
Other Authors: School of Chemical and Biomedical Engineering
Format: Article
Language:English
Published: 2017
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Online Access:https://hdl.handle.net/10356/86305
http://hdl.handle.net/10220/43993
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-863052023-12-29T06:49:53Z Deep neural network-based bandwidth enhancement of photoacoustic data Gutta, Sreedevi Kadimesetty, Venkata Suryanarayana Kalva, Sandeep Kumar Pramanik, Manojit Ganapathy, Sriram Yalavarthy, Phaneendra K. School of Chemical and Biomedical Engineering Photoacoustic data Deep neural network Photoacoustic (PA) signals collected at the boundary of tissue are always band-limited. A deep neural network (DNN) was proposed to enhance the bandwidth of the detected PA signal, thereby improving the quantitative accuracy of the reconstructed PA images. A least square based deconvolution method that utilizes the Tikhonov regularization framework was used for comparison with the proposed network. The proposed method was evaluated using both numerical and experimental data. The results indicate that the proposed method was capable of enhancing the bandwidth of the detected PA signal, which inturn improves the contrast recovery and quality of reconstructed PA images without adding any significant computational burden. NRF (Natl Research Foundation, S’pore) MOE (Min. of Education, S’pore) NMRC (Natl Medical Research Council, S’pore) Published version 2017-11-06T03:51:42Z 2019-12-06T16:20:04Z 2017-11-06T03:51:42Z 2019-12-06T16:20:04Z 2017 2017 Journal Article Gutta, S., Kadimesetty, V. S., Kalva, S. K., Pramanik, M., Ganapathy, S., & Yalavarthy, P. K. (2017). Deep neural network-based bandwidth enhancement of photoacoustic data. Journal of Biomedical Optics, 22(11), 116001-. 1083-3668 https://hdl.handle.net/10356/86305 http://hdl.handle.net/10220/43993 10.1117/1.JBO.22.11.116001 202644 en Journal of Biomedical Optics © 2017 Society of Photo-optical Instrumentation Engineers (SPIE). This paper was published in Journal of Biomedical Optics and is made available as an electronic reprint (preprint) with permission of SPIE. The published version is available at: [http://dx.doi.org/10.1117/1.JBO.22.11.116001]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law. 8 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Photoacoustic data
Deep neural network
spellingShingle Photoacoustic data
Deep neural network
Gutta, Sreedevi
Kadimesetty, Venkata Suryanarayana
Kalva, Sandeep Kumar
Pramanik, Manojit
Ganapathy, Sriram
Yalavarthy, Phaneendra K.
Deep neural network-based bandwidth enhancement of photoacoustic data
description Photoacoustic (PA) signals collected at the boundary of tissue are always band-limited. A deep neural network (DNN) was proposed to enhance the bandwidth of the detected PA signal, thereby improving the quantitative accuracy of the reconstructed PA images. A least square based deconvolution method that utilizes the Tikhonov regularization framework was used for comparison with the proposed network. The proposed method was evaluated using both numerical and experimental data. The results indicate that the proposed method was capable of enhancing the bandwidth of the detected PA signal, which inturn improves the contrast recovery and quality of reconstructed PA images without adding any significant computational burden.
author2 School of Chemical and Biomedical Engineering
author_facet School of Chemical and Biomedical Engineering
Gutta, Sreedevi
Kadimesetty, Venkata Suryanarayana
Kalva, Sandeep Kumar
Pramanik, Manojit
Ganapathy, Sriram
Yalavarthy, Phaneendra K.
format Article
author Gutta, Sreedevi
Kadimesetty, Venkata Suryanarayana
Kalva, Sandeep Kumar
Pramanik, Manojit
Ganapathy, Sriram
Yalavarthy, Phaneendra K.
author_sort Gutta, Sreedevi
title Deep neural network-based bandwidth enhancement of photoacoustic data
title_short Deep neural network-based bandwidth enhancement of photoacoustic data
title_full Deep neural network-based bandwidth enhancement of photoacoustic data
title_fullStr Deep neural network-based bandwidth enhancement of photoacoustic data
title_full_unstemmed Deep neural network-based bandwidth enhancement of photoacoustic data
title_sort deep neural network-based bandwidth enhancement of photoacoustic data
publishDate 2017
url https://hdl.handle.net/10356/86305
http://hdl.handle.net/10220/43993
_version_ 1787136664208932864