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SUMMARY:ECE PhD Proposal Review: Yaoshen Yuan
DESCRIPTION:PhD Proposal Review: Enhancements for Monte Carlo Light Modeling Method and Its Applications in Near-infrared-based Brain Techniques \nYaoshen Yuan \nLocation: Microsoft Teams Link \nAbstract: Studying light propagation in biological tissues is critical for developing biophotonics techniques and its applications. Monte Carlo (MC) method\, a stochastic solver for radiative transfer equation\, has been recognized as the gold standard for modeling light propagation in turbid media. However\, due to the stochastic nature of MC method\, millions even billions of photons are usually required to achieve accurate results using MC method\, leading to a long computational time even with the acceleration using graphical processing units (GPU).\nFurthermore\, due to the rapid advances in multi-scale optical imaging techniques such as optical coherence tomography (OCT) and multiphoton microscopy (MPM)\, there is an increasing need to model light propagation in extremely complex tissues such as vessel networks. The mesh-based Monte Carlo (MMC) is usually superior than the voxel-based MC method for such modeling since unlike grid-like voxels\, tetrahedral meshes can represent arbitrary shapes with curved boundaries. However\, the mesh density can be excessively high when the tissue structure is extremely complex\, resulting in high computational costs and memory demand. The goal of this proposal is to focus on solving the challenges mentioned above. \nTo tackle the first challenge\, we came up with a filtering approach with GPU acceleration to improve the signal-to-noise ratio (SNR) of the results while keeping the simulated photons low. The adaptive non-local means (ANLM) filter is selected to suppress the stochastic noise in MC results because 1) the filtering process on each voxel is mutually independent\, making it possible for parallel computing; 2) it has high performance in denoising and a strong capacity in edge-preserving. For the second problem\, a novel method\, implicit mesh-based Monte Carlo (iMMC)\, was proposed to significantly reduce the mesh density. The iMMC utilizes the edge\, node and face of the tetrahedral mesh to model tissue structures with shapes of cylinder\, sphere and thin layer. The typical applications for edge\, node and face-based iMMC are vessel networks\, porous media and membranes\, respectively. Lastly\, we applied MC simulations and aforementioned filter on segmented brain models derived from MRI neurodevelopmental atlas to estimate the light dosage for transcranial photobiomodulation (t-PBM)\, a technique for treating major depressive disorder using near infrared\, across lifespan. Furthermore\, a new approach that can improve the penetration depth in optical brain imaging as well as PBM is proposed. In this approach\, the possibility of placing light sources in head cavities is investigated using MC simulations. The preliminary results demonstrate a better performance in deep brain monitoring compared to the standard transcranial approach using 10-20 EEG positioning system. \n 
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-yaoshen-yuan/
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