Towards a Fast and Safe LED-Based Photoacoustic Imaging Using Deep Convolutional Neural Network

Emran Mohammad Abu Anas, Haichong K. Zhang, Jin Kang, Emad Boctor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
EditorsAlejandro F. Frangi, Gabor Fichtinger, Julia A. Schnabel, Carlos Alberola-López, Christos Davatzikos
PublisherSpringer Verlag
Pages159-167
Number of pages9
ISBN (Print)9783030009366
DOIs
Publication statusPublished - Jan 1 2018
Event21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain
Duration: Sep 16 2018Sep 20 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11073 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
CountrySpain
CityGranada
Period9/16/189/20/18

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Keywords

  • Convolutional neural networks
  • Densenet
  • Laser
  • LED
  • Photoacoustic
  • Super-resoluton

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Anas, E. M. A., Zhang, H. K., Kang, J., & Boctor, E. (2018). Towards a Fast and Safe LED-Based Photoacoustic Imaging Using Deep Convolutional Neural Network. In A. F. Frangi, G. Fichtinger, J. A. Schnabel, C. Alberola-López, & C. Davatzikos (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings (pp. 159-167). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11073 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-00937-3_19