MLDIPS: enabling improved PS-OCT contrast through maximum likelihood estimation

Georgia L. Jones; Shadi Masoumi; Maxina Sheft; Jaeyul Lee; Brett E. Bouma; Martin Villiger

doi:10.1117/12.3005517

17 April 2024 MLDIPS: enabling improved PS-OCT contrast through maximum likelihood estimation

Georgia L. Jones, Shadi Masoumi, Maxina Sheft, Jaeyul Lee, Brett E. Bouma, Martin Villiger

Proceedings Volume PC12830, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVIII; PC128300D (2024) https://doi.org/10.1117/12.3005517
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

Conventional dual-input state PS-OCT incorrectly assumes that the two probing input states provide equally reliable measurements. In this work, we overcome this assumption by adapting a maximum-likelihood framework which combines all input state and spectral bin measurements to find the most likely sample Jones matrix. This processing method (MLDIPS) shows a significantly reduced retardance noise floor as well as improved qualitative characterization of white matter versus grey matter in porcine brain tissue, displaying better contrast to conventional dual-input processing.

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Citation Download Citation

Georgia L. Jones, Shadi Masoumi, Maxina Sheft, Jaeyul Lee, Brett E. Bouma, and Martin Villiger "MLDIPS: enabling improved PS-OCT contrast through maximum likelihood estimation", Proc. SPIE PC12830, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVIII, PC128300D (17 April 2024); https://doi.org/10.1117/12.3005517

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KEYWORDS

Biological samples

Birefringence

Brain tissue

Biological research

Optical coherence tomography

Polarimetry

Polarization

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