Robust photoacoustic beamforming using dense convolutional neural networks

Emran Mohammad Abu Anas, Haichong K. Zhang, Chloé Audigier, Emad Boctor

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

Abstract

Photoacoustic (PA) is a promising technology for imaging of endogenous tissue chromophores and exogenous contrast agents in a wide range of clinical applications. The imaging technique is based on excitation of a tissue sample using short light pulse, followed by acquisition of the resultant acoustic signal using an ultrasound (US) transducer. To reconstruct an image of the tissue from the received US signals, the most common approach is to use the delay-and-sum (DAS) beamforming technique that assumes a wave propagation with a constant speed of sound. Unfortunately, such assumption often leads to artifacts such as sidelobes and tissue aberration; in addition, the image resolution is degraded. With an aim to improve the PA image reconstruction, in this work, we propose a deep convolutional neural networks-based beamforming approach that uses a set of densely connected convolutional layers with dilated convolution at higher layers. To train the network, we use simulated images with various sizes and contrasts of target objects, and subsequently simulating the PA effect to obtain the raw US signals at an US transducer. We test the network on an independent set of 1,500 simulated images and we achieve a mean peak-to-signal-ratio of 38.7 dB between the estimated and reference images. In addition, a comparison of our approach with the DAS beamforming technique indicates a statistical significant improvement of the proposed technique.

Original languageEnglish (US)
Title of host publicationSimulation, Image Processing, and Ultrasound Systems for Assisted Diagnosis and Navigation - International Workshops, POCUS 2018, BIVPCS 2018, CuRIOUS 2018, and CPM 2018, Held in Conjunction with MICCAI 2018, Proceedings
EditorsStephen Aylward, Amber Simpson, Lena Maier-Hein, Anne Martel, Matthieu Chabanas, João Manuel Tavares, Ingerid Reinertsen, Zeike Taylor, Yiming Xiao, Keyvan Farahani, Danail Stoyanov, Shuo Li, Hassan Rivaz
PublisherSpringer Verlag
Pages3-11
Number of pages9
ISBN (Print)9783030010447
DOIs
Publication statusPublished - Jan 1 2018
EventInternational Workshop on Point-of-Care Ultrasound, POCUS 2018, the International Workshop on Bio-Imaging and Visualization for Patient-Customized Simulations, BIVPCS 2017, the International Workshop on Correction of Brainshift with Intra-Operative Ultrasound, CuRIOUS 2018, and the International Workshop on Computational Precision Medicine, CPM 2018, held in conjunction with the 21st International Conference on Medical Imaging 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)
Volume11042 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherInternational Workshop on Point-of-Care Ultrasound, POCUS 2018, the International Workshop on Bio-Imaging and Visualization for Patient-Customized Simulations, BIVPCS 2017, the International Workshop on Correction of Brainshift with Intra-Operative Ultrasound, CuRIOUS 2018, and the International Workshop on Computational Precision Medicine, CPM 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018
CountrySpain
CityGranada
Period9/16/189/20/18

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Keywords

  • Beamforming
  • Convolutional neural networks
  • Delay-and-sum
  • Dense convolution
  • Dilated convolution
  • Photoacoustic

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Anas, E. M. A., Zhang, H. K., Audigier, C., & Boctor, E. (2018). Robust photoacoustic beamforming using dense convolutional neural networks. In S. Aylward, A. Simpson, L. Maier-Hein, A. Martel, M. Chabanas, J. M. Tavares, I. Reinertsen, Z. Taylor, Y. Xiao, K. Farahani, D. Stoyanov, S. Li, ... H. Rivaz (Eds.), Simulation, Image Processing, and Ultrasound Systems for Assisted Diagnosis and Navigation - International Workshops, POCUS 2018, BIVPCS 2018, CuRIOUS 2018, and CPM 2018, Held in Conjunction with MICCAI 2018, Proceedings (pp. 3-11). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11042 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-01045-4_1