MCDataset: a public reference dataset of Monte Carlo simulated quantities for multilayered and voxelated tissues computed by massively parallel PyXOpto Python package

Abstract

. Significance: Current open-source Monte Carlo (MC) method implementations for light propagation modeling are many times tedious to build and require third-party licensed software that can often discourage prospective researchers in the biomedical optics community from fully utilizing the light propagation tools. Furthermore, the same drawback also limits rigorous cross-validation of physical quantities estimated by various MC codes. Aim: Proposal of an open-source tool for light propagation modeling and an easily accessible dataset to encourage fruitful communications amongst researchers and pave the way to a more consistent comparison between the available implementations of the MC method. Approach: The PyXOpto implementation of the MC method for multilayered and voxelated tissues based on the Python programming language and PyOpenCL extension enables massively parallel computation on numerous OpenCL-enabled devices. The proposed implementation is used to compute a large dataset of reflectance, transmittance, energy deposition, and sampling volume for various source, detector, and tissue configurations. Results: The proposed PyXOpto agrees well with the original MC implementation. However, further validation reveals a noticeable bias introduced by the random number generator used in the original MC implementation. Conclusions: Establishing a common dataset is highly important for the validation of existing and development of MC codes for light propagation in turbid media.

Relation between fluence rate and mean photons pathlengths: an interesting option for Monte Carlo-based fluence calculations in biomedical optics

“…[1][2][3][4][5] Until nowadays, the modelization of photon fluence rate Φ( r) with Monte Carlo (MC) simulation has mainly been performed by using the method originally conceived by Wang et al 6 and widely used in biomedical optics. [7][8][9][10][11][12] The method is based on the determination of the fraction of absorbed light power in a small tissue volume. However, there are other available options to assess Φ( r).…”

scatterBrains: an open database of human head models and companion optode locations for realistic Monte Carlo photon simulations

“…24,30,32,[42][43][44] This is driven in part by difficulties in manually placing optodes and potentially limits the works' investigative scope. Recently, both Nizam et al 45 and Bürmen et al 46 have acknowledged the need in the field for open reference datasets, as well as user-friendly multiple optode placement, for both machine learning training and validation of custom MC software. Both groups have provided datasets based on slab geometries with embedded absorbing targets of various shapes meant for training tomographic reconstruction algorithms.…”

“…Recently, both Nizam et al 45 . and Bürmen et al 46 . have acknowledged the need in the field for open reference datasets, as well as user-friendly multiple optode placement, for both machine learning training and validation of custom MC software.…”

Relation between fluence rate and mean photons pathlengths: an alternative option for Monte Carlo-based-calculations of fluence

“…[1][2][3][4][5] Until nowadays, the modeling of photon fluence rate Φ( r) with Monte Carlo (MC) simulations in biomedical optics has mainly been performed with the method originally conceived by Wang et al 6 and largely applied since many years. [7][8][9][10][11][12] The method uses and exploits the determination of the fraction of absorbed light power in a small tissue volume. However, Φ( r) can be assessed by other available methods.…”