An open-source platform for optical coherence tomography monte carlo simulation

Maltais-Tariant, Raphaël; Erde, Khaliun; Kanniyappan, Udayakumar; Boudoux, Caroline; Dehaes, Mathieu

doi:10.1117/12.2576943

Cited by 2 publications

(1 citation statement)

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“…Over the years, several works have been carried out to make MC simulations adaptable for various applications in optics [25]. MC simulation was modified to simulate the conditions of Frequency Domain Optical Tomography [26], OCT [27], Polarization of light, and Raman scattering [18,28,29]. Additionally, the photon trajectory effects due to changes in the simulation geometry are also implemented and studied in MC simulations.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-enabled soft tissue tumor localization using spatially offset Raman spectral analysis: in-silico investigations

Jayasankar¹,

Bajhaiya²,

Sujatha³

2022

J. Phys. D: Appl. Phys.

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Raman spectroscopy and its derivatives have gained wide acceptance among optical biopsy tools for tissue discrimination. However, the identification and localization of subsurface soft tissue tumors are still challenging. Several designs for the Raman probe have been proposed to this effect, among which Spatially Offset Raman Spectroscopy (SORS) could offer a potential solution. This paper attempts to demonstrate the simultaneous identification of subsurface adenoma depth and thickness using Convolutional Neural Networks applied on Monte Carlo simulated SORS signals. The application of transfer learning model resulted in a better Root Mean Square Error (RMSE) of 4.40% for depth prediction as compared to the 7 to 25 % RMSE demonstrated by previous reports. Simultaneous thickness prediction is demonstrated for the first time with 8.42% RMSE.

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Section: Introductionmentioning

confidence: 99%

Deep learning-enabled soft tissue tumor localization using spatially offset Raman spectral analysis: in-silico investigations

Jayasankar¹,

Bajhaiya²,

Sujatha³

2022

J. Phys. D: Appl. Phys.

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MCOCT: an experimentally and numerically validated, open-source Monte Carlo simulator for optical coherence tomography

Erdenedalai,

Maltais-Tariant,

Dehaes

et al. 2024

Biomed. Opt. Express

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Here, we present MCOCT, a Monte Carlo simulator for optical coherence tomography (OCT), incorporating a Gaussian illumination scheme and bias to increase backscattered event collection. MCOCT optical fluence was numerically compared and validated to an established simulator (MCX) and showed concordance at the focus while diverging slightly with distance to it. MCOCT OCT signals were experimentally compared and validated to OCT signals acquired in tissue-mimicking phantoms with known optical properties and showed a similar attenuation pattern with increasing depth while diverging beyond 1.5 mm and proximal to layer interfaces. MCOCT may help in the design of OCT systems for a wide range of applications.

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An open-source platform for optical coherence tomography monte carlo simulation

Cited by 2 publications

References 0 publications

Deep learning-enabled soft tissue tumor localization using spatially offset Raman spectral analysis: in-silico investigations

Deep learning-enabled soft tissue tumor localization using spatially offset Raman spectral analysis: in-silico investigations

MCOCT: an experimentally and numerically validated, open-source Monte Carlo simulator for optical coherence tomography

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