Deep neural network-based bandwidth enhancement of photoacoustic data

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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Bibliographic Details
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
Subjects:
Photoacoustic data
Deep neural network
Online Access:https://hdl.handle.net/10356/86305
http://hdl.handle.net/10220/43993
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Institution: Nanyang Technological University
Language: English
Description
Summary: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.

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