Influence of the phase function on determination of the optical properties of biological tissue by spatially resolved reflectance

Kienle, Alwin; Forster, Florian K.; Hibst, Raimund

doi:10.1364/ol.26.001571

Cited by 87 publications

(62 citation statements)

References 16 publications

(3 reference statements)

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“…C is a normalization constant such that (2.3) hold. We mention that scattering kernels other than (2.4) have also been used in some situations [36] and that simplified (FokkerPlanck) models can also be used to analyze highly peaked scattering in biological tissues [37].…”

Section: Problem Formulationmentioning

confidence: 99%

Frequency Domain Optical Tomography Based on the Equation of Radiative Transfer

Ren

Bal

Hielscher³

2006

SIAM J. Sci. Comput.

139

112

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Abstract. Optical tomography consists of reconstructing the spatial distribution of absorption and scattering properties of a medium from surface measurements of transmitted light intensities. Mathematically, this problem amounts to parameter identification for the equation of radiative transfer (ERT) with diffusion-type boundary measurements. Because they are posed in the phase-space, radiative transfer equations are quite challenging to solve computationally. Most past works have considered the steady-state ERT or the diffusion approximation of the ERT. In both cases, substantial cross-talk has been observed in the reconstruction of the absorption and scattering properties of inclusions. In this paper, we present an optical tomographic reconstruction algorithm based on the frequency-domain ERT. The inverse problem is formulated as a regularized least-squares minimization problem, in which the mismatch between forward model predictions and measurements is minimized. The ERT is discretized by using a discrete ordinates method for the directional variables and a finite volume method for the spatial variables. A limited-memory quasi-Newton algorithm is used to minimize the least-squares functional. Numerical simulations with synthetic data show that the cross-talk between the two optical parameters is significantly reduced in reconstructions based on frequency-domain data as compared to those based on steady-state data.

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Section: Problem Formulationmentioning

confidence: 99%

Frequency Domain Optical Tomography Based on the Equation of Radiative Transfer

Ren

Bal

Hielscher³

2006

SIAM J. Sci. Comput.

139

112

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show abstract

“…The diffusion approximation is valid for highly scattering media and for large source-to-detector separations, where high photon scattering renders their transport deterministic. Transport is governed by the reduced scattering coefficient and the absorption coefficient; consequently, information pertaining to the specific phase function is lost 18 and scattering can be convolved with absorption effects if reflectance is not measured at multiple source-to-detector separations, or equivalently, at multiple spatial frequencies. Spatially resolved, analytical solutions to the steady state diffusion approximation have been derived by Farrell and Dognitz in the real and spatial-frequency domains, respectively.…”

Section: Introductionmentioning

confidence: 99%

System analysis of spatial frequency domain imaging for quantitative mapping of surgically resected breast tissues

et al. 2013

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Abstract. The feasibility of spatial frequency domain imaging (SFDI) for breast surgical margin assessment was evaluated in tissue-simulating phantoms and in fully intact lumpectomy specimens at the time of surgery. Phantom data was evaluated according to contrast-detail resolution, quantitative accuracy and model-data goodness of fit, where optical parameters were estimated by minimizing the residual sum of squares between the measured modulation amplitude and its solutions, modeled according to diffusion and scaled-Monte Carlo simulations. In contrast-detail phantoms, a 1.25-mm-diameter surface inclusion was detectable for scattering contrast >28%; a fraction of this scattering contrast (7%) was detectable for a 10 mm surface inclusion and at least 33% scattering contrast was detected up to 1.5 mm below the phantom surface, a probing depth relevant to breast surgical margin assessment. Recovered hemoglobin concentrations were insensitive to changes in scattering, except for overestimation at visible wavelengths for total hemoglobin concentrations <15 μM. The scattering amplitude increased linearly with scattering concentration, but the scattering slope depended on both the particle size and number density. Goodness of fit was comparable for the diffusion and scaled-Monte Carlo models of transport in spatially modulated, near-infrared reflectance acquired from 47 lumpectomy tissues, but recovered absorption parameters varied more linearly with expected hemoglobin concentration in liquid phantoms for the scaled-Monte Carlo forward model. SFDI could potentially reduce the high secondary excision rate associated with breast conserving surgery; its clinical translation further requires reduced image reconstruction time and smart inking strategies. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…[1][2][3][4][5][6] Along with this increased sensitivity comes the need for improved models of light scattering that are both versatile and contribute improved insights into tissue characterization. However, despite the direct link between scattering and fundamental tissue ultrastructure, many models of light scattering focus on the role of wavelength-dependent empirical parameters, which determine the shape of PðθÞ, rather than the more insightful physical properties.…”

Section: Introductionmentioning

confidence: 99%

“…4,[22][23][24] In this paper, we present a unified model to quantify six physical tissue properties (three ultrastructural and three microvascular) from a single spectral measurement of the spatial reflectance profile pðr; λÞ at subdiffusion lengthscales. Toward this end, there are four primary goals of the current work: (1) to present a unified model (using two previously developed models) that relates light propagation in biological media to the underlying tissue ultrastructure and microvasculature; (2) to provide a new empirical Monte Carlo-based model that enables rapid generation of pðr; λÞ, specifically at subdiffusion lengthscales; (3) to demonstrate the application of a newly developed inverse algorithm to extract physical properties from a measurement of pðr; λÞ; and (4) to announce MATLAB codes posted online 25 for use by other researchers, who wish to carry out the methods and analysis presented in this work.…”

Section: Introductionmentioning

confidence: 99%

Subdiffusion reflectance spectroscopy to measure tissue ultrastructure and microvasculature: model and inverse algorithm

Radosevich¹,

Eshein²,

Nguyen³

et al. 2015

J. Biomed. Opt

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Abstract. Reflectance measurements acquired from within the subdiffusion regime (i.e., lengthscales smaller than a transport mean free path) retain much of the original information about the shape of the scattering phase function. Given this sensitivity, many models of subdiffusion regime light propagation have focused on parametrizing the optical signal through various optical and empirical parameters. We argue, however, that a more useful and universal way to characterize such measurements is to focus instead on the fundamental physical properties, which give rise to the optical signal. This work presents the methodologies that used to model and extract tissue ultrastructural and microvascular properties from spatially resolved subdiffusion reflectance spectroscopy measurements. We demonstrate this approach using ex-vivo rat tissue samples measured by enhanced backscattering spectroscopy. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Influence of the phase function on determination of the optical properties of biological tissue by spatially resolved reflectance

Cited by 87 publications

References 16 publications

Frequency Domain Optical Tomography Based on the Equation of Radiative Transfer

Frequency Domain Optical Tomography Based on the Equation of Radiative Transfer

System analysis of spatial frequency domain imaging for quantitative mapping of surgically resected breast tissues

Subdiffusion reflectance spectroscopy to measure tissue ultrastructure and microvasculature: model and inverse algorithm

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