Bayesian analysis of depth resolved OCT attenuation coefficients

Lionel D. Fiske, Maurice C.G. Aalders*, Mitra Almasian, Ton G. van Leeuwen, Aggelos K. Katsaggelos, Oliver Cossairt, Dirk J. Faber

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Optical coherence tomography (OCT) is an optical technique which allows for volumetric visualization of the internal structures of translucent materials. Additional information can be gained by measuring the rate of signal attenuation in depth. Techniques have been developed to estimate the rate of attenuation on a voxel by voxel basis. This depth resolved attenuation analysis gives insight into tissue structure and organization in a spatially resolved way. However, the presence of speckle in the OCT measurement causes the attenuation coefficient image to contain unrealistic fluctuations and makes the reliability of these images at the voxel level poor. While the distribution of speckle in OCT images has appeared in literature, the resulting voxelwise corruption of the attenuation analysis has not. In this work, the estimated depth resolved attenuation coefficient from OCT data with speckle is shown to be approximately exponentially distributed. After this, a prior distribution for the depth resolved attenuation coefficient is derived for a simple system using statistical mechanics. Finally, given a set of depth resolved estimates which were made from OCT data in the presence of speckle, a posterior probability distribution for the true voxelwise attenuation coefficient is derived and a Bayesian voxelwise estimator for the coefficient is given. These results are demonstrated in simulation and validated experimentally.

Original languageEnglish (US)
Article number2263
JournalScientific reports
Volume11
Issue number1
DOIs
StatePublished - Dec 2021

ASJC Scopus subject areas

  • General

Fingerprint Dive into the research topics of 'Bayesian analysis of depth resolved OCT attenuation coefficients'. Together they form a unique fingerprint.

Cite this