Convolutional dictionary learning for blind deconvolution of optical coherence tomography images

Junzhe Wang, Brendt Wohlberg, R. B.A. Adamson

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

In this study, we demonstrate a sparsity-regularized, complex, blind deconvolution method for removing sidelobe artefacts and stochastic noise from optical coherence tomography (OCT) images. Our method estimates the complex scattering amplitude of tissue on a line-by-line basis by estimating and deconvolving the complex, one-dimensional axial point spread function (PSF) from measured OCT A-line data. We also present a strategy for employing a sparsity weighting mask to mitigate the loss of speckle brightness within tissue-containing regions caused by the sparse deconvolution. Qualitative and quantitative analyses show that this approach suppresses sidelobe artefacts and background noise better than traditional spectral reshaping techniques, with negligible loss of tissue structure. The technique is particularly useful for emerging OCT applications where OCT images contain strong specular reflections at air-tissue boundaries that create large sidelobe artefacts.

Original languageEnglish
Pages (from-to)1834-1854
Number of pages21
JournalBiomedical Optics Express
Volume13
Issue number4
DOIs
Publication statusPublished - Apr 1 2022

Bibliographical note

Funding Information:
Acknowledgements. The authors gratefully acknowledge support for this project from Michelle Bona, Josh Farrell, Matthew Farrell, Alex Hackett, Drew Hubley, Matthew Jahns, Kaila Kelly, Dan MacDougall, Darren Oickle.

Publisher Copyright:
© 2022 Optica Publishing Group.

ASJC Scopus Subject Areas

  • Biotechnology
  • Atomic and Molecular Physics, and Optics

PubMed: MeSH publication types

  • Journal Article

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