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 was proposed to enhance the bandwidth (BW) of the detected PA signal, thereby improving the quantitative accuracy of the reconstructed PA images. A least square-based deconvolution method th...

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Bibliographic Details
Published in:	Journal of biomedical optics Vol. 22; no. 11; p. 116001
Main Authors:	Gutta, Sreedevi, Kadimesetty, Venkata Suryanarayana, Kalva, Sandeep Kumar, Pramanik, Manojit, Ganapathy, Sriram, Yalavarthy, Phaneendra K
Format:	Journal Article
Language:	English
Published:	United States Society of Photo-Optical Instrumentation Engineers 01-11-2017
Subjects:	Least-Squares Analysis Neural Networks (Computer) Photoacoustic Techniques - methods Photoacoustic Techniques - standards reconstruction bandwidth enhancement photoacoustic data deep neural network
Online Access:	Get full text
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Summary:	Photoacoustic (PA) signals collected at the boundary of tissue are always band-limited. A deep neural network was proposed to enhance the bandwidth (BW) 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 BW of the detected PA signal, which inturn improves the contrast recovery and quality of reconstructed PA images without adding any significant computational burden.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1083-3668 1560-2281
DOI:	10.1117/1.JBO.22.11.116001