Deep neural network-based bandwidth enhancement of photoacoustic data

Sreedevi Gutta; Venkata Suryanarayana Kadimesetty; Sandeep Kumar Kalva; Manojit Pramanik; Sriram Ganapathy; Phaneendra K. Yalavarthy

doi:10.1117/1.JBO.22.11.116001

2 November 2017 Deep neural network-based bandwidth enhancement of photoacoustic data

Sreedevi Gutta, Venkata Suryanarayana Kadimesetty, Sandeep Kumar Kalva, Manojit Pramanik, Sriram Ganapathy, Phaneendra K. Yalavarthy

Author Affiliations +

Funded by: Department of Biotechnology (DBT) Innovative Young Biotechnologist Award (IYBA), DBT Bioengineering, Ministry of Education in Singapore, Singapore Ministry of Healths National Medical Research Council, Singapore Ministry of Health’s National Medical Research Council, Ministry of Health (MOH)

Journal of Biomedical Optics, Vol. 22, Issue 11, 116001 (November 2017). https://doi.org/10.1117/1.JBO.22.11.116001

Abstract

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.

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Sreedevi Gutta, Venkata Suryanarayana Kadimesetty, Sandeep Kumar Kalva, Manojit Pramanik, Sriram Ganapathy, and Phaneendra K. Yalavarthy "Deep neural network-based bandwidth enhancement of photoacoustic data," Journal of Biomedical Optics 22(11), 116001 (2 November 2017). https://doi.org/10.1117/1.JBO.22.11.116001

Received: 28 July 2017; Accepted: 9 October 2017; Published: 2 November 2017

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