Accurate Monte Carlo simulation of frequency‐domain optical coherence tomography

Wang, Yan; Bai, Li

doi:10.1002/cnm.3177

Cited by 12 publications

(6 citation statements)

References 21 publications

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“…Simulations of wave scattering and signal localization in OCT can be based on first-principles calculations using Maxwell's equations, although this method is currently computationally prohibitive [19], [34], [35]. Explicitly accounting for light source geometry and signal localization in frequency-domain OCT in the Monte Carlo method may further improve the fidelity of the estimated OCT signal [36]. Efforts are currently underway to expand the implementation of the Monte Carlo to situations where significant spatial variation in the scattering phase function p(θ, ϕ) is expected [37], as is the case in glandular mucosa.…”

Section: Classification Accuracymentioning

confidence: 99%

Transfer learning from simulations improves the classification of OCT images of glandular epithelia

Ostvar

Troung

Silver

et al. 2020

Preprint

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Esophageal adenocarcinoma (EAC) is a rare but lethal cancer with rising incidence in several global hotspots including the United States. The five-year survival rate for patients diagnosed with advanced disease can be as low as 5% in EAC, making early detection and preventive intervention crucial. The current standard of care for EAC targets patients with Barrett's esophagus (BE), the main precursor to EAC and a relatively common condition in adults with chronic acid reflux disease. Preventive care for EAC requires repeated surveillance endoscopies of BE patients with biopsy sampling, and can be intrusive, error-prone, and costly. The integration of minimally-invasive subsurface tissue imaging in the current standard of care can reduce the need for exhaustive tissue sampling and improve the quality of life in BE patients. Effective adoption of subsurface imaging in EAC care can be facilitated by computer-aided detection (CAD) systems based on deep learning. Despite their recent successes in lung and breast cancer imaging, the development of deep neural networks for rare conditions like EAC remains challenging due to data scarcity, heavy bias in existing datasets toward non-cases, and uncertainty in image labels. Here we explore the use of synthetic datasets--specifically data derived from simulations of optical back-scattering during imaging-- in the development of CAD systems based on deep learning. As a proof of concept, we studied the binary classification of esophageal OCT into normal squamous and glandular mucosae, typical of BE. We found that deep convolutional networks trained on synthetic data had improved performance over models trained on clinical datasets with uncertain labels. Model performance also improved with dataset size during training on synthetic data. Our findings demonstrate the utility of transfer from simulations to real data in the context of medical imaging, especially in the severely data-poor regime and when significant uncertainty in labels are present, and motivate further development of transfer learning from simulations to aid the development of CAD for rare malignancies.

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Section: Classification Accuracymentioning

confidence: 99%

Transfer learning from simulations improves the classification of OCT images of glandular epithelia

Ostvar

Troung

Silver

et al. 2020

Preprint

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“…In general, light transport by Monte Carlo method is performed in models that consider turbid homogeneous media or multilayer. 27,28 But if it is considered that the refraction index is similar for different biological tissues (skin, brain, liver, kidneys, etc. ), then a homogeneous media can be simulated, using absorption and scattering coefficients that represent various tissues and blood as a "generic tissue."…”

Section: Lu-cr In Tissuementioning

confidence: 99%

Theoretical and experimental characterization of emission and transmission spectra of Cerenkov radiation generated by 177Lu in tissue

et al. 2019

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Cerenkov radiation (CR) is the emission of UV-vis light generated by the de-excitation of the molecules in the medium, after being polarized by an excited particle traveling faster than the speed of light. When β particles travel through tissue with energies greater than 219 keV, CR occurs. Tissues possess a spectral optical window of 600 to 1100 nm. The CR within this range can be useful for quantitative preclinical studies using optical imaging and for the in-vivo evaluation of 177 Lu-radiopharmaceuticals (β-particle emitters). The objective of our research was to determine the experimental emission light spectrum of 177 Lu-CR and evaluate its transmission properties in tissue as well as the feasibility to applying CR imaging in the preclinical studies of 177 Lu-radiopharmaceuticals. The theoretical and experimental characterizations of the emission and transmission spectra of 177 Lu-CR in tissue, in the vis-NIR region (350 to 900 nm), were performed using Monte Carlo simulation and UV-vis spectroscopy. Mice 177 Lu-CR images were acquired using a charge-coupled detector camera and were quantitatively analyzed. The results demonstrated good agreement between the theoretical and the experimental 177 Lu-CR emission spectra. Preclinical CR imaging demonstrated that the biokinetics of 177 Lu-radiopharmaceuticals in the main organs of mice can be acquired.

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“…Monte Carlo (MC) is one of the most popular numerical simulation tools in biophotonics, recognized as the gold standard for modeling light-tissue interactions. 5,6 Many studies also utilize MC to simulate the imaging process and speckle patterns in FD-OCT. [7][8][9][10] However, in these methods, the photon step sizes are randomly sampled, hindering simulations from distinguishing between different speckle characteristics in static samples and dynamic medium, as all scattering points are assumed to be dynamic.…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo-based realistic modeling of speckles in Fourier-domain optical coherence tomography

Mao,

Ling,

Xue

et al. 2024

Optical Interactions With Tissue and Cells XXXV

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In the measurement of tissues using Fourier-domain optical coherence tomography (FD-OCT), speckle patterns from dynamic and static components often exhibit distinct characteristics: the former can be reduced through incoherent averaging, while the latter cannot. However, in the conventional Monte Carlo (MC) based simulations of FD-OCT, the speckle patterns of dynamic medium and static regions cannot be distinguished due to the random spatial distribution of scattering events across the entire simulated phantom. To tackle this issue, we propose a hybrid phantom model for MC-based realistic simulation of speckles in FD-OCT. In the simulation using the proposed model, static tissue within the 3D structure is modeled as a swarm of fixed particles loosely packed in the background medium. Once a photon is emitted into the static tissue model, it keeps moving until encountering a fixed particle and undergoing scattering. On the other hand, the spatial distribution of scattering points in the dynamic medium is still assumed random, which makes the photon's step size sampled based on the wavelength-dependent scattering coefficient. Compared to conventional MC simulations, speckles simulated with the proposed model at different time points exhibit a higher spatial correlation in the static structures, which allows them to remain after incoherent averaging. In contrast, speckles in the dynamic component manifest de-correlation across multiple simulations. Future works involve leveraging this method to simulate dynamic OCT and linking structural information with speckle patterns to solve inverse problems.

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Accurate Monte Carlo simulation of frequency‐domain optical coherence tomography

Cited by 12 publications

References 21 publications

Transfer learning from simulations improves the classification of OCT images of glandular epithelia

Transfer learning from simulations improves the classification of OCT images of glandular epithelia

Theoretical and experimental characterization of emission and transmission spectra of Cerenkov radiation generated by 177Lu in tissue

Monte Carlo-based realistic modeling of speckles in Fourier-domain optical coherence tomography

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