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Hybrid mesh and voxel based Monte Carlo algorithm for accurate and efficient photon transport modeling in complex bio-tissues

机译:基于混合网和体素的蒙特卡罗算法用于复杂生物组织中的精确高效的光子传输建模

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摘要

Over the past decade, an increasing body of evidence has suggested that three-dimensional (3-D) Monte Carlo (MC) light transport simulations are affected by the inherent limitations and errors of voxel-based domain boundaries. In this work, we specifically address this challenge using a hybrid MC algorithm, namely split-voxel MC or SVMC, that combines both mesh and voxel domain information to greatly improve MC simulation accuracy while remaining highly flexible and efficient in parallel hardware, such as graphics processing units (GPU). We achieve this by applying a marching-cubes algorithm to a pre-segmented domain to extract and encode sub-voxel information of curved surfaces, which is then used to inform ray-tracing computation within boundary voxels. This preservation of curved boundaries in a voxel data structure demonstrates significantly improved accuracy in several benchmarks, including a human brain atlas. The accuracy of the SVMC algorithm is comparable to that of mesh-based MC (MMC), but runs 2x-6x faster and requires only a lightweight preprocessing step. The proposed algorithm has been implemented in our open-source software and is freely available at http://mcx.space.
机译:在过去十年中,增加的证据表明,三维(3-D)蒙特卡罗(MC)光传输模拟受到基于体素的畴边界的固有局限性和误差的影响。在这项工作中,我们使用混合MC算法,即Split-Voxel MC或SVMC来解决这一挑战,即组合网格和体素域信息,以大大提高MC仿真精度,同时保持高度灵活,高效的并联硬件,例如图形处理单元(GPU)。我们通过将游行 - 立方体算法应用于预分段域来提取和编码曲面的子体素信息来实现这一目标,然后用于向边界体素内提供射线跟踪计算。这种体素数据结构中的弯曲边界的保存证明了几种基准中的准确性显着提高,包括人脑图集。 SVMC算法的准确性与基于网格的MC(MMC)相当,但仅运行2x-6x,并且只需要轻量级预处理步骤。所提出的算法已在我们的开源软件中实现,并在http://mcx.space自由提供。

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