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5 March 2021 Reducing boundary voxelization errors of 3-D monte carlo photon simulation
Shijie Yan, Qianqian Fang
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Proceedings Volume 11634, Multimodal Biomedical Imaging XVI; 116340A (2021) https://doi.org/10.1117/12.2583185
Event: SPIE BiOS, 2021, Online Only
Abstract
The voxel-based Monte Carlo method (VMC) offers efficiency in modeling light transport in complex bio-tissues, but is known to produce erroneous results due to its terraced boundaries. We present a significantly improved VMC by incorporating mesh-based boundary information in a hybrid modeling approach. A fast preprocessing step first extracts surface meshes from an arbitrary voxelated domain using the marching-cubes algorithm. An extended voxel format is developed to encode oblique surface information while keeping the data structure efficient for parallel processing. This enables modeling of subvoxel boundaries, resulting in significantly improved accuracy in benchmarks, including an MRI human brain atlas.
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Shijie Yan and Qianqian Fang "Reducing boundary voxelization errors of 3-D monte carlo photon simulation", Proc. SPIE 11634, Multimodal Biomedical Imaging XVI, 116340A (5 March 2021); https://doi.org/10.1117/12.2583185
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KEYWORDS
Monte Carlo methods
Image segmentation
Tissues
Photon transport
3D modeling
Biomedical optics
Computer simulations
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