Bayesian maximum likelihood estimator of phase retardation for quantitative polarization-sensitive optical coherence tomography

Kasaragod, Deepa; Makita, Shuichi; Fukuda, Shin–ichi; Beheregaray, Simone; Oshika, Tetsuro; Yasuno, Yoshiaki

doi:10.1364/oe.22.016472

Cited by 41 publications

(37 citation statements)

References 52 publications

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“…The depth-resolved birefringence (local retardation) tomography was obtained by local Jones matrix analysis [24] combined with a maximum a posteriori birefringence estimator [25]. For easy observation, a pseudo-color birefringence tomography is created by combining the scattering intensity, birefringence, and reliability of the birefringence estimation.…”

Section: Multi-contrast Image Formationmentioning

confidence: 99%

“…In this image, the birefringence is expressed by color hue if it is reliable, otherwise the pixel is a shade of gray. The birefringence computation protocol is detailed in Section 4 of [22], and the pseudo-color image formation is described in Section 3.4 of [25].…”

Section: Multi-contrast Image Formationmentioning

confidence: 99%

“…6(d) would be artifacts caused by birefringence measurement and/or estimation algorithm. One possible cause is that the current birefringence estimator [25] does not take the speckle effect into account. Although it was not evident as in Fig.…”

Section: Multi-contrast Imaging Of the Inner-forearmmentioning

confidence: 99%

“…Therefore, a depth-localized birefringence measurement is required for more accurate dermatological investigation. The limitation of cumulative phase retardation imaging can be overcome by the birefringence measurement based on localized Jones matrix measurement [23][24][25]. Although the local Jones matrix measurement suffers from a low signalto-noise ratio and inaccurate phase-retardation and birefringence, these problems were recently resolved by birefringence and phase retardation estimation techniques that include the coherent averaging of the Jones matrix and/or Jones vector [26][27][28], eigen-value-based averaging [29], Jones matrix estimation based on the Cloude-Pottier decomposition [30], Muller analysis [31,32], a specially designed phase-retardation estimation function [33], and maximum a posteriori birefringence estimators [25,34,35].…”

Section: Introductionmentioning

confidence: 99%

“…It enables the simultaneous investigation of the polarization and flow properties of skin. The JM-OCT simultaneously provides depth-resolved birefringence (local phase retardation) tomography [25], degree-ofpolarization-uniformity (DOPU) [40,41], complex-correlation based OCT-A [42], and the scattering intensity OCT. The system is based on a design that is similar to our previous passive-polarization-delay based posterior-eye JM-OCT [22,27], but is specially redesigned for dermatological investigation.…”

Section: Introductionmentioning

confidence: 99%

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Three-dimensional multi-contrast imaging of in vivo human skin by Jones matrix optical coherence tomography

Makita

Hong

et al. 2017

Biomed. Opt. Express

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A custom made dermatological Jones matrix optical coherence tomography (JM-OCT) is presented. It uses a passive-polarization-delay component based swept-source JM-OCT configuration, but is specially designed for in vivo human skin measurement. The center wavelength of its probe beam is 1310 nm and the A-line rate is 49.6 kHz. The JM-OCT is capable of simultaneously providing birefringence (local retardation) tomography, degree-ofpolarization-uniformity tomography, complex-correlation-based optical coherence angiography, and conventional scattering OCT. To evaluate the performance of this JM-OCT, we measured in vivo human skin at several locations. Using the four kinds of OCT contrasts, the morphological characteristics and optical properties of different skin types were visualized.

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Section: Multi-contrast Image Formationmentioning

confidence: 99%

Section: Multi-contrast Image Formationmentioning

confidence: 99%

Section: Multi-contrast Imaging Of the Inner-forearmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Three-dimensional multi-contrast imaging of in vivo human skin by Jones matrix optical coherence tomography

Makita

Hong

et al. 2017

Biomed. Opt. Express

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Improved accuracy of quantitative birefringence imaging by polarization sensitive OCT with simple noise correction and its application to neuroimaging

Baumann

Harper

Eugui

et al. 2021

Journal of Biophotonics

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Polarization‐sensitive optical coherence tomography (PS‐OCT) enables three‐dimensional imaging of biological tissues based on the inherent contrast provided by scattering and polarization properties. In fibrous tissue such as the white matter of the brain, PS‐OCT allows quantitative mapping of tissue birefringence. For the popular PS‐OCT layout using a single circular input state, birefringence measurements are based on a straight‐forward evaluation of phase retardation data. However, the accuracy of these measurements strongly depends on the signal‐to‐noise ratio (SNR) and is prone to mapping artifacts when the SNR is low. Here we present a simple yet effective approach for improving the accuracy of PS‐OCT phase retardation and birefringence measurements. By performing a noise bias correction of the detected OCT signal amplitudes, the impact of the noise floor on retardation measurements can be markedly reduced. We present simulation data to illustrate the influence of the noise bias correction on phase retardation measurements and support our analysis with real‐world PS‐OCT image data.

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Maximum likelihood estimation in the context of an optical measurement

Vella

Alonso

2020

Progress in Optics

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The method of maximum likelihood estimation (MLE) is a widely used statistical approach for estimating the values of one or more unknown parameters of a probabilistic model based on observed data. In this tutorial, I briefly review the mathematical foundations of MLE, then reformulate the problem for the measurement of a spatially-varying optical intensity distribution. In this context, the detection of each individual photon is treated as a random event, the outcome being the photon's location. A typical measurement consists of many detected photons, which accumulate to form a spatial intensity profile. Here, I show a straightforward derivation for the likelihood function and Fisher information matrix (FIM) associated with a measurement of multiple photons incident on a detector comprised of a discrete array of pixels. An estimate for the parameter(s) of interest may then be obtained by maximizing the likelihood function, while the FIM determines the uncertainty of the estimate. To illustrate these concepts, several simple examples are presented for the one-and twoparameter cases, revealing many interesting properties of the MLE formalism, as well as some practical considerations for optical experiments. Throughout these examples, connections are also drawn to optical applications of quantum weak measurements, including off-null ellipsometry and scatterometry.

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Bayesian maximum likelihood estimator of phase retardation for quantitative polarization-sensitive optical coherence tomography

Cited by 41 publications

References 52 publications

Three-dimensional multi-contrast imaging of in vivo human skin by Jones matrix optical coherence tomography

Three-dimensional multi-contrast imaging of in vivo human skin by Jones matrix optical coherence tomography

Improved accuracy of quantitative birefringence imaging by polarization sensitive OCT with simple noise correction and its application to neuroimaging

Maximum likelihood estimation in the context of an optical measurement

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