Light Transport Through Biological Tissue

Monte Carlo Modeling of Light Transport in Multilayered Tissues (MCML) by Dr. Lihong Wang and Dr. Steven Jacques is a tool used to simulate the light transport in biological tissues. It can provide diffuse reflectance, diffuse transmittance and absorbance in a multi-layered tissue structure. It can also be used to obtain the absorbance of light inside tissue as well, which can be useful for photoacoustic imaging. The simulation is statistical and hence requires large number of photons to be tracked to reduce variation. Since photon is treated as a particle, the phase and polarization is ignored. The aim of MCML is to look at radiant energy distribution in a turbid medium where phase and polarization is quickly randomized and hardly have any role to play in the optical output. Currently MCML simulations exist for infant brain, superficial blood vessels, etc. Modifications of basic MCML are time-resolved MCML, GPU speeded-up MCML and Mesh-based MCML. Here is a recent review article on the advances of Monte Carlo simulation by Periyasamy et al., IEEE Reviews on Biomedial Engineering (2017).

MCML with Embedded Objects (MCML-EO):

We have modified the MCML to incorporate embedded object such as spherical, cylindrical, ellipsoidal or cuboidal shaped object. This modified MCML - we call it MCML-EO (MCML with Embedded Object) will be useful for modeling more realistic tissue models, such as lymph nodes, tumor, blood vessels, capillaries, bones, head, and other body parts. Mesh based Monte Carlo (MMC) can also be used for such modelling as it gives the flexibility of incorporating any shapes. However, MMC is more complex to modify and takes longer time to run. More details can be found in Periyasamy et al., Journal of Biomedical Optics 19 (2014). The codes can be downloaded from the Software Download page.

MCML-EO simulations to optimise photoacoustic imaging:

MCML-EO was used for modelling light distribution turbid medium to mimic sentinel lymph node (SLN) structure in photoacoustic imaging. SLN was modelled as embedded sphere in tissues. Simulations were done to optimise the best suitable light delivery scheme for SLN imaging using clinical array based ultrasonic probe with fiber based light delivery. More details can be found in Periyasamy et al., Journal of Biomedical Optics 18 (2013).

Flow chart for MCML-EO:

Light absorbance inside the SLN:

MCML-EO for Raman scattering:

We have also modified the MCML code to simulate Raman scattering. This was done in a multi-layered cuboid container filled with explosive material. The simulation would help to find out how various configuration of source and detector can help in optimizing the Raman Signal captured and noninvasively detect the explosive material inside the container. It was shown that with detectors placed far from the source fiber (in the transmission mode), the Raman signature from the material was better captured compared to the Raman signal from the container itself. Therefore, capturing the Raman signal from all around the object may have better sensitivity compared to traditional SORS technique, where typically the source and detector are located in the same plane. More details can be found in Periyasamy et al., Journal of Raman Spectroscopy 46 (2015). RMCC codes can be downloaded from the Software Download page.

Flow chart for RMCC (Raman Monte Carlo for Cuboid):

Raman photon distribution from all around the cuboid:

Time domain OCT simulation for layers with embedded objects:

Monte Carlo simulation for light propagation for time domain Optical Coherence Tomography (OCT) was done. The challenge is the simulation time. Since, the number of photon packets needs to simulated is very high in OCT, an improved Important Sampling (IS) method was used. Multi layered tissue with embedded objects (spherical, cylindrical, ellipsoid, cuboid) were considered. B-scan OCT images of Class I and Class II photons are simulated with reasonable computation time with a desktop. More details can be found in Periyasamy et al., Applied Optics 55 (2016). The codes can be downloaded from the Software Download page.

Flow chart for OCT with EO:

Class I and Class II photon signal intensity in a layer of tissue with embedded spherical object in it:

Optimization of light delivery for clinical photoacoustic system:

Translating photoacoustic imaging into clinical setup is a challenge. In this work, we report an integrated photoacoustic and ultrasound imaging system by combining the light delivery to the tissue with the ultrasound probe. Firstly, Monte Carlo simulations were run to study the variation in absorbance within tissue for different angles of illumination, fiber-to-probe distance, and fiber-to-tissue distance. This is followed by simulation for different depths of the embedded sphere (object of interest). Several probe holders were designed for different light launching angles. Phantoms were developed to mimic sentinel lymph node imaging scenario. It was observed that, for shallower imaging depths the variation in signal-to-noise ratio (SNR) values could be as high as 100% depending on the angle of illumination at a fixed fiber-to-probe distance and fiber to tissue distance. Results confirm that different light illumination angles are required for different imaging depth to get the highest SNR photoacoustic images. The results also validate that one can use Monte Carlo simulation as a tool to optimize the probe holder design depending on the imaging needs. This eliminates a trial and error approach generally used for designing a probe holder. More details can be found in Sivasubramanian et al., Journal of Biomedical Optics 22 (2017).

Imaging depth comparison between NIR-I and NIR-II window:

Photoacoustic imaging provides high resolution and high optical contrast imaging beyond optical diffusion limit. Further improvement in imaging depth has been achieved by using near infrared window-I (NIR-I, 700-900 nm) for illumination, due to lower scattering and absorption by tissues in this wavelength range. Recently, near infrared window-II (NIR-II, 900-1700 nm) has been explored for photoacoustic imaging. The imaging depths in biological tissues for different illumination wavelengths in visible, NIR-I, and NIR-II regions have been studied using Monte Carlo (MC) simulations and validated with experimental results. MC simulations were done to compute fluence in tissue, absorbance in blood vessel, and in a spherical absorber (mimicking sentinel lymph node) embedded at different depths in breast tissue. Photoacoustic tomography and acoustic resolution photoacoustic microscopy experiments were conducted to validate the MC results. We demonstrate that maximum imaging depth is achieved by wavelengths in NIR-I window (~800 nm) when the energy density deposited is same for all wavelengths. However, illumination using wavelengths around 1064 nm (NIR-II window) gives the maximum imaging depth when the energy density deposited is proportional to maximum permissible exposure (MPE) at corresponding wavelength. These results show that it is the higher MPE of NIR-II window that helps in increasing the photoacoustic imaging depth for chromophores embedded in breast tissue. More details can be found in Sharma et al., Journal of Biomedical Optics 24 (2019).