Importance sampling-based Monte Carlo simulation of time-domain optical coherence tomography with embedded objects

Periyasamy, Vijitha; Pramanik, Manojit

doi:10.1364/ao.55.002921

Cited by 20 publications

(16 citation statements)

References 38 publications

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“…Another drawback is that the computation for solving the inverse problem can be very costly, since a large quantity of photon packets have to be traced in simulation in order to obtain a sufficient number of photons back scattered from the photon‐tissue interaction for every depth of the complete scan range. To tackle the inefficiency problem, importance sampling has been applied to the photon scattering step to greatly enhance the chance for photon packets being back‐scattered towards the photon detector …”

Section: Related Workmentioning

confidence: 99%

“…To tackle the inefficiency problem, importance sampling has been applied to the photon scattering step to greatly enhance the chance for photon packets being back-scattered towards the photon detector. 12,16 Some MC simulation studies focused on simulating FDOCT. 17,18 In the FDOCT simulation, the light source is modeled as one with a broad wavelength bandwidth.…”

Section: Related Workmentioning

confidence: 99%

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Accurate Monte Carlo simulation of frequency‐domain optical coherence tomography

Wang

Bai

2019

Numer Methods Biomed Eng

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Optical coherence tomography (OCT) relies on optical interferometry to provide noninvasive imaging of living tissues. In addition to its 3D imaging capacity for medical diagnosis, its potential use for recovering optical parameters of biological tissues for biological and pathological analyses has also been explored by researchers, as pathological changes in tissue alter the microstructure of the tissue and therefore its optical properties. We aim to develop a new approach to OCT data analysis by estimating optical properties of tissues from OCT scans, which are invisible in the scans. This is an inverse problem. Solving an inverse problem involves a forward modeling step to simulate OCT scans of the tissues with hypothesized optical parameter values and an inverse step to estimate the real optical par1meters values by matching the simulated scans to real scans. In this paper, we present a Monte Carlo (MC)–based approach for simulating the frequency‐domain OCT. We incorporated a focusing Gaussian light beam rather than an infinitesimally thin light beam for accurate simulations. A new and more accurate photon detection scheme is also implemented. We compare our MC model to an analytical OCT model based on the extended Huygens‐Fresnel principle (EHF) to demonstrate the consistency between the two models. We show that the two models are in good agreement for tissues with high scattering and high anisotropy factors.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Accurate Monte Carlo simulation of frequency‐domain optical coherence tomography

Wang

Bai

2019

Numer Methods Biomed Eng

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“…, the photon is recorded as a class I photon, otherwise it is recorded as a class II photon [10,13].…”

Section: Mesh-based Monte Carlo For Optical Coherence Tomography (Mmcmentioning

confidence: 99%

“…Monte Carlo (MC) simulation is the golden is the golden means to simulate the interaction between light and tissue [9,10]. Many efficient MC models have been developed to study OCT signals of different tissues [11][12][13][14] or polarizationsensitive OCT [11,15]. However, there are few literatures to analyze OCT signals in brain tissue using MC technology.…”

Section: Introductionmentioning

confidence: 99%

A Mesh-Based Monte Carlo Study for Investigating Structural and Functional Imaging of Brain Tissue Using Optical Coherence Tomography

Yi¹,

Sun²,

Zou³

et al. 2019

Applied Sciences

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Optical coherence tomography (OCT) can obtain high-resolution three-dimensional (3D) structural images of biological tissues, and spectroscopic OCT, which is one of the functional extensions of OCT, can also quantify chromophores of tissues. Due to its unique features, OCT has been increasingly used for brain imaging. To support the development of the simulation and analysis tools on which OCT-based brain imaging depends, a model of mesh-based Monte Carlo for OCT (MMC-OCT) is presented in this work to study OCT signals reflecting the structural and functional activities of brain tissue. In addition, an approach to improve the quantitative accuracy of chromophores in tissue is proposed and validated by MMC-OCT simulations. Specifically, the OCT-based brain structural imaging was first simulated to illustrate and validate the MMC-OCT strategy. We then focused on the influences of different wavelengths on the measurement of hemoglobin concentration C, oxygen saturation Y, and scattering coefficient S in brain tissue. Finally, it is proposed and verified here that the measurement accuracy of C, Y, and S can be improved by selecting appropriate wavelengths for calculation, which contributes to the experimental study of brain functional sensing.

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“…However, they are in general slow, requiring thousands of samples to converge. To accelerate convergence, Jarabo et al [2014] introduced three techniques for uniform sampling in the temporal domain targeted to bidirectional methods, while Lima et al [2011] and Periyasamy and Pramanik [2016] proposed importance sampling strategies in the context of Optical Coherence Tomography. These techniques are designed to work in the presence of participating media; this is a particularly interesting case for transient imaging, since one of its key applications is seeing through such media (fog, murky water, etc).…”

Section: Light Transport Simulation Algorithmsmentioning

confidence: 99%

Recent advances in transient imaging: A computer graphics and vision perspective

et al. 2017

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Figure 1: Left: In 1964, Harold Edgerton captured the iconic Bullet Through Apple image ( c MIT Museum). The bullet traveled at about 850 m/s, which translated into an exposure of approximately 4-10 millionth of a second. Right: Almost 50 years later, the femto-photography technique was introduced [Velten et al. 2013], capable of capturing light in motion, with an effective exposure time of one trillionth of a second. The large split image is a composite of the three complete frames shown in the insets. The complete videos of this and other scenes can be downloaded from AbstractTransient imaging has recently made a huge impact in the computer graphics and computer vision fields. By capturing, reconstructing, or simulating light transport at extreme temporal resolutions, researchers have proposed novel techniques to show movies of light in motion, see around corners, detect objects in highly-scattering media, or infer material properties from a distance, to name a few. The key idea is to leverage the wealth of information in the temporal domain at the pico or nanosecond resolution, information usually lost during the capture-time temporal integration. This paper presents recent advances in this field of transient imaging from a graphics and vision perspective, including capture techniques, analysis, applications and simulation.

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Importance sampling-based Monte Carlo simulation of time-domain optical coherence tomography with embedded objects

Cited by 20 publications

References 38 publications

Accurate Monte Carlo simulation of frequency‐domain optical coherence tomography

Accurate Monte Carlo simulation of frequency‐domain optical coherence tomography

A Mesh-Based Monte Carlo Study for Investigating Structural and Functional Imaging of Brain Tissue Using Optical Coherence Tomography

Recent advances in transient imaging: A computer graphics and vision perspective

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