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|Title:||Deep neural network-based bandwidth enhancement of photoacoustic data||Authors:||Gutta, Sreedevi
Kadimesetty, Venkata Suryanarayana
Kalva, Sandeep Kumar
Yalavarthy, Phaneendra K.
Deep neural network
|Issue Date:||2017||Source:||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-.||Series/Report no.:||Journal of Biomedical Optics||Abstract:||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.||URI:||https://hdl.handle.net/10356/86305
|ISSN:||1083-3668||DOI:||10.1117/1.JBO.22.11.116001||Rights:||© 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.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCBE Journal Articles|
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