Monte Carlo eXtreme, or MCX, is an open-source Monte Carlo (MC) photon simulator for modeling light transport in 3D turbid media. The MCX/MMC Suite also contains mesh-based Monte Carlo (MMC) - an anatomically accurate 3D Monte Carlo (MC) simulator that can use a tetrahedral mesh to represent extremely complex tissue structures.
Both MCX and MMC supports graphics processing units (GPUs) and are capable of simulating thousands of photons simultaneously, making them among the fastest and most accurate photon modeling tools. Since their initial releases, the MCX Suite has become one of the most widely disseminated biophotonics modeling platforms, well-known for its high accuracy, high speed and versatility, as attested to by its over 27,000 downloads and nearly 1,000 citations from a large (2,400 registered users) world-wide user community. This rapidly expanding user community presents a diverse set of demands and a wide range of applications in the form of continuously evolving complex imaging domains and modalities. We are dedicated to serving this active and growing research community, and draw strong motivation from our extensive interactions with users, aiming to further expand the breadth, complexity, and efficiency of our simulation platform.
The development of MCX/MMC simulation platform is funded by the NIH/NIGMS under grant R01-GM114365.
"Iso2Mesh" is a MATLAB/Octave-based mesh generation toolbox, designed for easy creation of high quality surface and tetrahedral meshes from 3D volumetric images. It contains over 200 mesh processing scripts/programs, working either independently or interacting with external free meshing utilities. Iso2Mesh toolbox can directly convert a 3D image stack, including binary, segmented or gray-scale images such as MRI or CT scans, into quality volumetric meshes. This makes it particularly suitable for multi-modality medical imaging data analysis and multi-physics modeling. Above all, iso2mesh is open-source. You can download it for free. You are also allowed to extend the toolbox for your own research and share with other users. Iso2Mesh is cross-platform and is compatible with both MATLAB and GNU Octave (a free MATLAB clone).
Between 2009 and 2020, Iso2Mesh has been cited over 620 times, with over 4,500 registered users worldwide, and an accumulative download over 40,000.
The details of this toolbox can be found in the following papers (citing the first paper is highly encouraged):
Currently, Iso2Mesh and its submodules are broadly distributed among popular open-source MATLAB toolboxes, especially among major neuroimaging tools, including
JSONLab is a free and open-source JSON/UBJSON/MessagePack encoder and decoder written in the native MATLAB language. It can be used to convert a MATLAB data structure (array, struct, cell, struct array, cell array, and objects) into JSON/UBJSON/MessagePack formatted strings and files, or to parse a JSON/UBJSON/MessagePack file into MATLAB data structure. JSONLab supports both MATLAB and GNU Octave (a free MATLAB clone).
We envision that both JSON and its binary counterparts will play important rules not only for light-weight data storage, but also for storage and interchange of scientific data. It has both the flexibility and generality as in other general-purpose file specifications, such as HDF5 but has significantly reduced complexity and excellent readability.
Towards this goal, we have developed the JData Specification (http://github.com/fangq/jdata) to standardize serializations of complex scientific data structures, such as N-D arrays, sparse/complex-valued arrays, trees, maps, tables and graphs using JSON/binary JSON constructs. The text and binary formatted JData files are syntactically compatible with JSON/UBJSON formats, and can be readily parsed using existing JSON and UBJSON parsers. JSONLab is not just a parser and writer of JSON/UBJSON data files, but one that systematically converts complex scientific data structures into human-readable and universally supported JSON forms using the standardized JData data annotations.
OpenJData is a set of open data-standards based on JSON/binary JSON formats. They are self-documenting, highly extensible, versatile, lighweight and easy-to-implement.
The Brain2Mesh toolbox provides a streamlined matlab function to convert a segmented brain volumes and surfaces into a high-quality multi-layered tetrahedral brain/full head mesh. Typical inputs include segmentation outputs from SPM, FreeSurfer, FSL etc. This tool does not handle the segmentation of MRI scans, but examples of how commonly encountered segmented datasets can be used to create meshes can be found in the package named brain2mesh-demos.
The details of this toolbox can be found in the following paper:
|Brain mesh library||https://github.com/OpenJData/BrainMeshLibrary|