Improved velocimetry in optical coherence tomography using Bayesian analysis.

Published

Journal Article

OCT is a popular cross-sectional microscale imaging modality in medicine and biology. While structural imaging using OCT is a mature technology in many respects, flow and motion estimation using OCT remains an intense area of research. In particular, there is keen interest in maximizing information extraction from the complex-valued OCT signal. Here, we introduce a Bayesian framework into the data workflow in OCT-based velocimetry. We demonstrate that using prior information in this Bayesian framework can significantly improve velocity estimate precision in a correlation-based, model-based framework for Doppler and transverse velocimetry. We show results in calibrated flow phantoms as well as in vivo in a Drosophila melanogaster (fruit fly) heart. Thus, our work improves upon the current approaches in terms of improved information extraction from the complex-valued OCT signal.

Full Text

Duke Authors

Cited Authors

  • Zhou, KC; Huang, BK; Tagare, H; Choma, MA

Published Date

  • December 2015

Published In

Volume / Issue

  • 6 / 12

Start / End Page

  • 4796 - 4811

PubMed ID

  • 26713195

Pubmed Central ID

  • 26713195

Electronic International Standard Serial Number (EISSN)

  • 2156-7085

International Standard Serial Number (ISSN)

  • 2156-7085

Digital Object Identifier (DOI)

  • 10.1364/BOE.6.004796

Language

  • eng