TY - GEN
T1 - Towards a Fast and Safe LED-Based Photoacoustic Imaging Using Deep Convolutional Neural Network
AU - Anas, Emran Mohammad Abu
AU - Zhang, Haichong K.
AU - Kang, Jin
AU - Boctor, Emad M.
N1 - Funding Information:
We would like to thank the National Institute of Health for funding this project.
Funding Information:
Acknowledgements. We would like to thank the National Institute of Health for funding this project.
Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - The current standard photoacoustic (PA) technology is based on heavy, expensive and hazardous laser system for excitation of a tissue sample. As an alternative, light emitting diode (LED) offers safe, compact and inexpensive light source. However, the PA images of an LED-based system significantly suffer from low signal-to-noise-ratio due to limited LED-power. With an aim to improve the quality of PA images, in this work we propose to use deep convolutional neural networks that is built upon a previous state-of-the-art image enhancement approach. The key contribution is to improve the optimization of the network by guiding its feature extraction at different layers of the architecture. In addition to using a high quality target image at the output of the network, multiple target images with intermediate qualities are employed at in-betweens layers of the architecture to guide the feature extraction. We perform an end-to-end training of the network using a set of 4,536 low quality PA images from 24 experiments. On the test set from 15 experiments, we achieve a mean peak signal-to-noise ratio of 34.5 dB and a mean structural similarity index of 0.86 with a gain in the frame rate of 6 times compared to the conventional approach.
AB - The current standard photoacoustic (PA) technology is based on heavy, expensive and hazardous laser system for excitation of a tissue sample. As an alternative, light emitting diode (LED) offers safe, compact and inexpensive light source. However, the PA images of an LED-based system significantly suffer from low signal-to-noise-ratio due to limited LED-power. With an aim to improve the quality of PA images, in this work we propose to use deep convolutional neural networks that is built upon a previous state-of-the-art image enhancement approach. The key contribution is to improve the optimization of the network by guiding its feature extraction at different layers of the architecture. In addition to using a high quality target image at the output of the network, multiple target images with intermediate qualities are employed at in-betweens layers of the architecture to guide the feature extraction. We perform an end-to-end training of the network using a set of 4,536 low quality PA images from 24 experiments. On the test set from 15 experiments, we achieve a mean peak signal-to-noise ratio of 34.5 dB and a mean structural similarity index of 0.86 with a gain in the frame rate of 6 times compared to the conventional approach.
KW - Convolutional neural networks
KW - Densenet
KW - LED
KW - Laser
KW - Photoacoustic
KW - Super-resoluton
UR - http://www.scopus.com/inward/record.url?scp=85053848955&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-00937-3_19
DO - 10.1007/978-3-030-00937-3_19
M3 - Conference contribution
AN - SCOPUS:85053848955
SN - 9783030009366
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 159
EP - 167
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
A2 - Frangi, Alejandro F.
A2 - Fichtinger, Gabor
A2 - Schnabel, Julia A.
A2 - Alberola-López, Carlos
A2 - Davatzikos, Christos
PB - Springer Verlag
T2 - 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Y2 - 16 September 2018 through 20 September 2018
ER -