Photoacoustic Source Detection and Reflection Artifact Removal Enabled by Deep Learning

@article{Allman2018PhotoacousticSD,
  title={Photoacoustic Source Detection and Reflection Artifact Removal Enabled by Deep Learning},
  author={Derek Allman and Austin Reiter and Muyinatu A. Lediju Bell},
  journal={IEEE Transactions on Medical Imaging},
  year={2018},
  volume={37},
  pages={1464-1477}
}
Interventional applications of photoacoustic imaging typically require visualization of point-like targets, such as the small, circular, cross-sectional tips of needles, catheters, or brachytherapy seeds. When these point-like targets are imaged in the presence of highly echogenic structures, the resulting photoacoustic wave creates a reflection artifact that may appear as a true signal. We propose to use deep learning techniques to identify these types of noise artifacts for removal in… CONTINUE READING

References

Publications referenced by this paper.
Showing 1-10 of 43 references

Medical ultrasound imaging using neural networks

  • M. Nikoonahad, D. C. Liv
  • Electron. Lett., vol. 26, no. 8, pp. 545–546, Apr…
  • 1990
Highly Influential
3 Excerpts

Photoacoustic image reconstruction via deep learning

  • S. Antholzer, M. Haltmeier, R. Nuster, J. Schwab
  • Proc. SPIE, vol. 10494, p. 104944U, Feb. 2018.
  • 2018
1 Excerpt

Reconstruction of initial pressure from limited view photoacoustic images using deep learning

  • D. Waibel, J. Gröhl, F. Isensee, T. Kirchner, K. Maier-Hein, L. Maier-Hein
  • Proc. SPIE, vol. 10494, p. 104942S, Feb. 2018.
  • 2018
1 Excerpt

Accuracy of a novel photoacoustic-based approach to surgical guidance performed with and without a da Vinci robot

  • N. Gandhi, S. Kim, P. Kazanzides, M.A.L. Bell
  • Proc. SPIE, vol. 10064, p. 100642V, Mar. 2017.
  • 2017

Similar Papers

Loading similar papers…