Generalized mesh-based Monte Carlo for wide-field illumination and detection via mesh retessellation.

Ruoyang Yao,Xavier Intes,Qianqian Fang

doi:10.1364/boe.7.000171

Ruoyang Yao, Xavier Intes + Show 1 more

Open Access

https://doi.org/10.1364/boe.7.000171

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Abstract

Monte Carlo methods are commonly used as the gold standard in modeling photon transport through turbid media. With the rapid development of structured light applications, an accurate and efficient method capable of simulating arbitrary illumination patterns and complex detection schemes over large surface area is in great need. Here we report a generalized mesh-based Monte Carlo algorithm to support a variety of wide-field illumination methods, including spatial-frequency-domain imaging (SFDI) patterns and arbitrary 2-D patterns. The extended algorithm can also model wide-field detectors such as a free-space CCD camera. The significantly enhanced flexibility of source and detector modeling is achieved via a fast mesh retessellation process that combines the target domain and the source/detector space in a single tetrahedral mesh. Both simulations of complex domains and comparisons with phantom measurements are included to demonstrate the flexibility, efficiency and accuracy of the extended algorithm. Our updated open-source software is provided at http://mcx.space/mmc.

Highlights

Optical imaging has become an invaluable tool in numerous biomedical applications to noninvasively quantify and monitor the functional, structural and molecular states of thick tissues [1, 2]
Compared to a previous paper in which we reported a wide-field Mesh-based Monte Carlo (MMC) implementation for axis-aligned parallel-beam (AAPB) pattern illumination [38], the new algorithm has several key
In the Results section, we evaluate both efficiency and accuracy of the new algorithm by comparing results with a voxel-based wide-field MC implementation – Monte Carlo eXtreme (MCX) [22] – and AAPB-MMC

Summary

Introduction

Optical imaging has become an invaluable tool in numerous biomedical applications to noninvasively quantify and monitor the functional, structural and molecular states of thick tissues [1, 2]. With increasing applications in the mesoscopic regime [5, 6] and emerging optical techniques such as imaging with early photons [7,8,9], there is a great interest in developing more accurate and computationally efficient forward models. The computational efficiency of MC modeling has been significantly enhanced via the development of effective MC formulations and algorithmic implementations New formulations such as perturbation MC (pMC) [13,14,15], adjoint method [16,17,18], seeding strategies [19] as well as rescaling techniques [20], coupled with the dramatic speed acceleration using modern parallel hardware, general purpose graphics processing units (GPU) [21, 22], have enabled performing MC-based simulations under a few minutes [23]. In optical tomography, MC-based forward models have been used successfully to image functional [14, 24], structural [15, 25, 26] and multiplexed molecular markers [27,28,29,30]

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Results

Conclusion