Shijie Yan PhD Proposal Review

Name:
Shijie Yan

Title:
Efficient Monte Carlo light transport algorithms in complex scattering media

Date:
7/29/2024

Time:
12:00:00 PM

Committee Members:
Prof. Qianqian Fang (Advisor)
Prof. Steven Jacques
Prof. David Kaeli
Prof. Edwin Marengo

Abstract:
Modeling light-tissue interactions is crucial for many optical imaging modalities, for which the Monte Carlo (MC) method has been widely recognized as the gold-standard. Despite dramatic speed improvements gained via the use of graphics processing units (GPUs), MC simulations remain computationally intensive. Efficient and accurate MC algorithms are needed to further consider physiologically realistic tissue models, especially for emerging optical imaging techniques. Voxel-based MC (VMC) and mesh-based MC (MMC) are two major MC methods for modeling complex tissues with their respective strengths and weaknesses. While VMC offers higher computational efficiency due to the simple data structure, its accuracy suffers from the terraced boundary shape especially in low-scattering medium; on the other side, MMC offers improved boundary fidelity but can be slow and memory-intensive, particularly at high mesh density. Furthermore, emerging wide-field diffuse optical imaging systems using structured light require more efficient modeling to handle numerous illumination patterns. Additionally, niche applications such as polarized light imaging could also benefit from many of the recent advances from modern MC simulations such as GPU acceleration and handling of complex heterogeneous media.

This proposal is aimed to push the frontiers of modern MC simulation algorithms to fundamentally enhance their utilities in diverse applications. To reduce the staircase effect in VMC, we have developed a hybrid MC algorithm, named split-voxel MC (SVMC), where sub-voxel oblique surfaces are extracted using a marching-cubes algorithm and are incorporated into a memory-efficient voxelated data structure. SVMC allows VMC to handle curved surfaces while remaining computationally efficient. A GPU-accelerated marching-cubes algorithm was also developed to further accelerate SVMC domain preprocessing. On the other hand, to further improve MMC computational efficiency, a dual-grid MMC (DMMC) algorithm was developed to perform fast ray-tracing inside a coarse tetrahedral mesh while saving fluence data over a dense voxelated grid, simultaneously achieving improved speed and output accuracy. To accommodate increasing needs of modeling wide-field pattern based sources, we have developed a “photon sharing’’ MC algorithm that performs simulations of all illumination and detection patterns in parallel, improving computational speed by an order of magnitude. Additionally, we have developed a GPU-accelerated massively-parallel algorithm capable of modeling Mie scattering of sphere particles in three-dimensional media for polarized light imaging, achieving nearly 1000$\times$ speed acceleration compared to sequential implementation.

Lastly, we have also investigated a hardware-accelerated MMC algorithm using the NVIDIA OptiX ray-tracing framework, leveraging modern GPU ray-tracing (RT) cores extensively optimized for graphics rendering. Preliminary results demonstrate comparable accuracy and significantly improved simulation speed compared to conventional tetrahedral MMC.