2 November 2017 Deep neural network-based bandwidth enhancement of photoacoustic data
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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.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Sreedevi Gutta, Sreedevi Gutta, Venkata Suryanarayana Kadimesetty, Venkata Suryanarayana Kadimesetty, Sandeep Kumar Kalva, Sandeep Kumar Kalva, Manojit Pramanik, Manojit Pramanik, Sriram Ganapathy, Sriram Ganapathy, Phaneendra K. Yalavarthy, 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 . Submission: Received: 28 July 2017; Accepted: 9 October 2017
Received: 28 July 2017; Accepted: 9 October 2017; Published: 2 November 2017
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