Advertisement

Compressed Sensing for Optical Coherence Tomography Angiography Volume Generation

  • Lennart HusvogtEmail author
  • Stefan B. Ploner
  • Daniel Stromer
  • Julia Schottenhamml
  • Eric Moult
  • James G. Fujimoto
  • Andreas Maier
Conference paper
  • 48 Downloads
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

Optical coherence tomography angiography (OCTA) is an increasingly popular modality for imaging of the retinal vasculature. Repeated optical coherence tomography (OCT) scans of the retina allow the computation of motion contrast to display the retinal vasculature. To the best of our knowledge, we present the first application of compressed sensing for the generation of OCTA volumes. Using a probabilistic signal model for the computation of OCTA volumes and a 3D median filter, it is possible to perform compressed sensing reconstruction of OCTA volumes while suppressing noise. The presented approach was tested on a ground truth, averaged from ten individual OCTA volumes. Average reductions of the mean squared error of 9:67% were achieved when comparing reconstructed OCTA images to the stand-alone application of a 3D median filter.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Literatur

  1. 1.
    Husvogt L, Ploner S, Maier A. Optical coherence tomography. In: Medical imaging systems. Springer, Cham; 2018. p. 251–261.Google Scholar
  2. 2.
    Choi W, Moult EM, Waheed NK, et al. Ultrahigh-speed, swept-source optical coherence tomography angiography in nonexudative age-related macular degeneration with geographic atrophy. Ophthalmology. 2015;122(12):2532–2544.Google Scholar
  3. 3.
    Novais EA, Adhi M, Moult EM, et al. Choroidal neovascularization analyzed on ultrahigh-speed swept-source optical coherence tomography angiography compared to spectral-domain optical coherence tomography angiography. Am J Ophthalmol. 2016;164:80–88.Google Scholar
  4. 4.
    Ploner SB, Kraus MF, Husvogt L, et al. 3-D OCT motion correction efficiently enhanced with OCT angiography. In: Investigative ophthalmology & visual science. vol. 59. The Association for Research in Vision and Ophthalmology; 2018. p. 3922.Google Scholar
  5. 5.
    Candès E, Wakin M. An introduction to compressive sampling. IEEE Signal Process Mag. 2008;.Google Scholar
  6. 6.
    Sidky EY, Pan X. Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization. Phys Med Biol. 2008;53(17):4777–4807.Google Scholar
  7. 7.
    Lustig M, Donoho D, Pauly JM. Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med. 2007;.Google Scholar
  8. 8.
    Ploner SB, Riess C, Schottenhamml J, et al. A joint probabilistic model for speckle variance, amplitude decorrelation and interframe variance (IFV) optical coherence tomography angiography. In: Bildverarbeitung für die Medizin 2018. 211279. Springer Berlin Heidelberg; 2018. p. 98–102.Google Scholar
  9. 9.
    Liu X, Kang JU. Compressive SD-OCT: the application of compressed sensing in spectral domain optical coherence tomography. Opt Express. 2010;18(21):22010–22019.Google Scholar
  10. 10.
    Manhart MT, Kowarschik M, Fieselmann A, et al. Dynamic iterative reconstruction for interventional 4-d c-arm CT perfusion imaging. IEEE Trans Med Imaging. 2013;32(7):1336–1348.Google Scholar
  11. 11.
    Lopuhaa HP, Rousseeuw PJ. Breakdown points of a fine equivariant estimators of multivariate location and covariance matrices. The Annals of Statistics. 1991;19(1):229–248.Google Scholar
  12. 12.
    Romano Y, Elad M, Milanfar P. The little engine that could: regularization by denoising (RED). SIAM J Imaging Sci. 2016;10(4):1804–1844.Google Scholar
  13. 13.
    Schottenhamml J, Moult EM, Novais EA, et al. OCT-OCTA segmentation. In: Bildverarbeitung für dieMedizin 2018. Berlin, Heidelberg: Springer Vieweg, Berlin, Heidelberg; 2018. p. 284.Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2020

Authors and Affiliations

  • Lennart Husvogt
    • 1
    • 2
    Email author
  • Stefan B. Ploner
    • 1
  • Daniel Stromer
    • 1
  • Julia Schottenhamml
    • 1
  • Eric Moult
    • 2
  • James G. Fujimoto
    • 2
  • Andreas Maier
    • 1
  1. 1.Pattern Recognition LabFriedrich-Alexander-Universität Erlangen-NürnbergErlangenDeutschland
  2. 2.Biomedical Optical Imaging and Biophotonics GroupMITCambridgeUSA

Personalised recommendations