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Doppler Optical Coherence Tomography of Retinal Circulation
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Improved velocimetry in optical coherence tomography using Bayesian analysis.

Kevin C Zhou1, Brendan K Huang1, Hemant Tagare2

  • 1Department of Biomedical Engineering, Yale University, 55 Prospect St., New Haven, Connecticut 06520, USA.

Biomedical Optics Express
|December 30, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian framework to enhance Optical Coherence Tomography (OCT) velocimetry, improving the precision of blood flow and motion estimates by better utilizing complex OCT signals.

Keywords:
(000.5490) Probability theory, stochastic processes, and statistics(030.6140) Speckle(110.4153) Motion estimation and optical flow(110.4500) Optical coherence tomography(170.3880) Medical and biological imaging(290.5820) Scattering measurements

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Area of Science:

  • Biomedical Imaging
  • Optical Coherence Tomography (OCT)
  • Fluid Dynamics

Background:

  • Optical Coherence Tomography (OCT) is a widely used cross-sectional imaging technique in medicine and biology.
  • Estimating flow and motion using OCT signals is an active research area, with a focus on extracting more information from complex OCT data.

Purpose of the Study:

  • To introduce a Bayesian framework to improve OCT-based velocimetry.
  • To enhance information extraction from complex-valued OCT signals for more precise velocity estimations.

Main Methods:

  • Developed and applied a Bayesian framework to the OCT data workflow.
  • Utilized prior information within a correlation-based, model-based framework for Doppler and transverse velocimetry.

Main Results:

  • Demonstrated significant improvements in velocity estimate precision.
  • Showcased enhanced information extraction from complex-valued OCT signals.
  • Validated results in calibrated flow phantoms and in vivo in Drosophila melanogaster (fruit fly) hearts.

Conclusions:

  • The proposed Bayesian framework significantly enhances OCT-based velocimetry.
  • This approach improves the precision of flow and motion estimation by effectively leveraging prior information and complex OCT signal characteristics.