MCX Cloud – a modern, scalable, high-performance and in-browser Monte Carlo simulation platform with cloud computing

Abstract

Significance: Despite the ample progress made towards faster and more accurate Monte Carlo (MC) simulation tools over the past decade, the limited usability and accessibility of these advanced modeling tools remain key barriers towards widespread use among the broad user community. Aim: An open-source, high-performance, web-based MC simulator that builds upon modern cloud computing architectures is highly desirable to deliver state-of-the-art MC simulations and hardware acceleration to general users without the need for special hardware installation and optimization. Approach: We have developed a configuration-free, in-browser 3-D MC simulation platform -- MCX Cloud -- built upon an array of robust and modern technologies, including a Docker Swarm-based cloud-computing backend and a web-based graphical user interface (GUI) that supports in-browser 3-D visualization, asynchronous data communication, and automatic data validation via JavaScript Object Notation (JSON) schemas. Results: The front-end of the MCX Cloud platform offers an intuitive simulation design, fast 3-D data rendering, and convenient simulation sharing. The Docker Swarm container orchestration backend is highly scalable and can support high-demand GPU MC simulations using Monte Carlo eXtreme (MCX) over a dynamically expandable virtual cluster. Conclusion: MCX Cloud makes fast, scalable, and feature-rich MC simulations readily available to all biophotonics researchers without overhead. It is fully open-source and can be freely accessed at http://mcx.space/cloud.

Using spectral derivatives to remove the influence of hair on optical images of the static absorbing properties of tissue-like turbid media

“…(4) varies with wavelength. The model employed the MCX package, a voxel-based open-source light transport simulator funded by the US National Institute of Health 17 . The model geometry is illustrated in Fig.…”

Wearable, high-density fNIRS and diffuse optical tomography technologies: a perspective

“…Standardization is essential to enabling easy sharing of data and ensuring comparable acquisition device performance. Future datasets will be recorded with wearable HD-DOT instruments built and tested according to international norms (see, for example, the developing fNIRS device standard IEC 80601-2-71) and will be stored using established, open-source international data recording formats [e.g., the shared near-infrared format, snirf 105 available in a GitHub repository at: https://github.com/fNIRS/snirf , the Brain Imaging Data Structure (BIDS), 106 NeuroJSON 107 ]. The signal processing streams will follow established standards, so the analysis of shared data can be repeated anywhere by anyone.…”

BlenderPhotonics – an integrated open-source software environment for 3-D meshing and photon simulations in complex tissues

“…4(a)) as an example to illustrate the simplicity of using JSON/JMesh data annotations to store mesh data. Because the JMesh format is built upon the JData specification 38, 44 – a lightweight standard using JSON to annotate scientific data, JMesh mesh data constructs also supports strongly-typed array (see Fig. 4c) and record-level binary data compression 44 (see Fig.…”

“…This module calls Iso2Mesh to automatically tessellate a 3-D volume stored in the NIfTI, 40 JNIfTI 41 or MATLAB .mat formats and output a surface mesh model for subsequent processing. NIfTI is an open-standard to store MRI/fMRI scans and is widely supported in the field of neuroimaging; JNIfTI is a standardized 41 JSON wrapper 44 to the NIfTI format to enable human-readability, easy extension and data-exchange between neuroimaging tools. By calling the streamlined mesh generation function in Iso2Mesh, the 3-D volume data from a JNIfTI/NIfTI file can be pre-processed to create both tetrahedral volume mesh as well as surface mesh that is at the outer surface of the volume mesh.…”