Troiani F et al. (2018), Simulating optical coherence tomography for obs...

XB-ART-55648

PLoS One 2018 Jan 01;137:e0200392. doi: 10.1371/journal.pone.0200392.

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Simulating optical coherence tomography for observing nerve activity: A finite difference time domain bi-dimensional model.

Troiani F , Nikolic K , Constandinou TG .

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We present a finite difference time domain (FDTD) model for computation of A line scans in time domain optical coherence tomography (OCT). The OCT output signal is created using two different simulations for the reference and sample arms, with a successive computation of the interference signal with external software. In this paper we present the model applied to two different samples: a glass rod filled with water-sucrose solution at different concentrations and a peripheral nerve. This work aims to understand to what extent time domain OCT can be used for non-invasive, direct optical monitoring of peripheral nerve activity.

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Species referenced: Xenopus laevis
Genes referenced: kidins220

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	Fig 1. Yee lattice with positions of various field components. The components of the electric field are in the middle of the edges and the components of the magnetic fields are in the centre of the faces. ©1966 IEEE. Reprinted, with permission, from [21].
	Fig 2. Frequency spectrum for pulses of different lengths, fitted with a Gaussian curve. In black the simulated pulse, in red the Gaussian fit. It is possible to notice that the broader spectrum is not a perfect Gaussian; this is due to the numerical dispersion being more pronounced in a source with a bigger frequency range.
	Fig 3. Refractive index of a water-sucrose solution as a function of the sucrose concentration at 589.3 nm [27]. In this work we are assuming that it is possible to translate the data to obtain values for different wavelengths.
	Fig 4. Peripheral nerve. Right: schematic structure of a nerve. Left: Xenopus Laevis’ sciatic nerve cross section [28]. It is possible to see how this nerve is made of a single bundle of axons surrounded by the perineum and loose epineurium.
	Fig 5. Simulation domain for the nerve model.
	Fig 6. OCT signal for source with a σλ ∼ 76 nm. The top panel shows the intensity OCT signal in arbitrary units, in this figure it is possible to discern the various elements inside the tissues: epineurium, elastic fibres and axons are labelled. The bottom panel shows the difference between the intensity OCT signals obtained from the simulations for the inactive and active nerves normalised to the intensity of the incoming light. It is noticeable that, as expected, the change in signal is only due to the change in refractive index of the axons. The axons themselves are not discernible because the resolution is not high enough. The zero has been placed at the interface between water and epineurium and the grey shadowed area represents the poisson noise.
	Fig 7. OCT signal for source with a σλ ∼ 13 nm. The top panel shows the intensity OCT signal in arbitrary units, in this it is not possible to discern all the various elements inside the tissues, as the resolution is not high enough. The bottom panel shows the difference between the intensity OCT signals obtained from the simulations for the inactive and active nerves normalised to the intensity of the incoming light. It is noticeable that, as expected, the change in signal is only due to the change in refractive index of the axons. The zero has been placed at the interface between water and epineurium and the grey shadowed area represents the poisson noise.
	Fig 8. OCT signal for different concentrations of water-sucrose solutions and σλ ∼ 76 nm. Top panel: signal obtained for the glass rods model with a water-sucrose solution at 0.1%. Other panels: difference between the signal obtained from different concentrations (0.1%, 0.01% and 0.001% respectively) and pure water normalised to the intensity of the incoming light. The grey shadowed area represents the poisson noise.
	Fig 9. Transmission coefficient calculated theoretically using Fresnel equations (red line) and from the simulations (blue points).

References [+] :

Aguirre, Depth-resolved imaging of functional activation in the rat cerebral cortex using optical coherence tomography. 2006, Pubmed