MCXLAB is the native MEX version of MCX for Matlab and GNU Octave. It compiles the entire MCX code into a MEX function which can be called directly inside Matlab or Octave. The input and output files in MCX are replaced by convenient in-memory struct variables in MCXLAB, thus, making it much easier to use and interact. Matlab/Octave also provides convenient plotting and data analysis functions. With MCXLAB, your analysis can be streamlined and speed- up without involving disk files.
Because MCXLAB contains the exact computational codes for the GPU calculations as in the MCX binaries, MCXLAB is expected to have identical performance when running simulations. By default, we compile MCXLAB with the support of recording detected photon partial path-lengths (i.e. the "make det" option). In addition, we also provide "mcxlab_atom": an atomic version of mcxlab compiled similarly as "make detbox" for MCX. It supports atomic operations using shared memory enabled by setting "cfg.sradius" input parameter to a positive number.
The system requirements for MCXLAB are the same as MCX: you have to make sure that you have a CUDA-capable graphics card with properly configured CUDA driver (you can run the standard MCX binary first to test if your system is capable to run MCXLAB). Of course, you need to have either Matlab or Octave installed.
MCXLAB needs libcudart.so.4 (for Unix-like systems) or cudart.dll (for Windows). For Linux/Mac, you need to set your LD_LIBRARY_PATH environment variable to contain the path to this library file.
To simplify the installation, we highly recommend you to link the libraries to your /usr/lib directory. For 64bit Linux, you can use the following command:
sudo ln -s /usr/local/cuda/lib64/libcudart.so.4 /usr/lib64
For windows, you simply copy the cudart.dll file to Windows\System32 folder. This file is typically stored under the C:\CUDA\bin directory.
Once you set up the CUDA library path, you can then add the "mcxlab" directory to your Matlab/Octave search path using the addpath command. If you want to add this path permanently, please use the "pathtool" command, or edit your startup.m (~/.octaverc for Octave).
If everything works ok, typing "help mcxlab" in Matlab/Octave will print the help information. If you see any error, particularly any missing libraries, please make sure you have downloaded the matching version built for your platform.
To learn the basic usage of MCXLAB, you can type
and enter in Matlab/Octave to see the help information regarding how to use this function. The help information is listed below. You can find the input/output formats and examples. The input cfg structure has very similar field names as the verbose command line options in MCX.
==================================================================== MCXLAB - Monte Carlo eXtreme (MCX) for MATLAB/GNU Octave -------------------------------------------------------------------- Copyright (c) 2010,2011 Qianqian Fang <fangq at nmr.mgh.harvard.edu> URL: http://mcx.sf.net ==================================================================== Format: [flux,detphoton]=mcxlab(cfg); Input: cfg: a struct, or struct array. Each element of cfg defines the parameters associated with a simulation. It may contain the following fields: *cfg.nphoton: the total number of photons to be simulated (integer) *cfg.vol: a 3D array specifying the media index in the domain *cfg.prop: an N by 4 array, each row specifies [mua, mus, g, n] in order. the first row corresponds to medium type 0 which is typically [0 0 1 1]. The second row is type 1, and so on. *cfg.tstart: starting time of the simulation (in seconds) *cfg.tstep: time-gate width of the simulation (in seconds) *cfg.tend: ending time of the simulation (in second) *cfg.srcpos: a 1 by 3 vector, the position of the source in grid unit *cfg.srcdir: a 1 by 3 vector, specifying the incident vector cfg.nblocksize: how many CUDA thread blocks to be used  cfg.nthread: the total CUDA thread number  cfg.maxgate: the num of time-gates per simulation cfg.session: a string for output file names (used when no return variables) cfg.seed: seed for the random number generator (integer)  cfg.maxdetphoton: maximum number of photons saved by the detectors  cfg.detpos: an N by 4 array, each row specifying a detector: [x,y,z,radius] cfg.respin: repeat simulation for the given time (integer)  cfg.gpuid: which GPU to use (run 'mcx -L' to list all GPUs)  cfg.isreflect: -consider refractive index mismatch, 0-matched index cfg.isrefint: 1-ref. index mismatch at inner boundaries, -matched index cfg.isnormalized:-normalize the output flux to unitary source, 0-no reflection cfg.issavedet: 1-to save detected photon partial path length, -do not save cfg.issave2pt: -to save flux distribution, 0-do not save cfg.issrcfrom0: 1-first voxel is [0 0 0], - first voxel is [1 1 1] cfg.isgpuinfo: 1-print GPU info, -do not print cfg.autopilot: 1-automatically set threads and blocks, -use nthread/nblocksize cfg.minenergy: terminate photon when weight less than this level (float) [0.0] cfg.unitinmm: defines the length unit for a grid edge length [1.0] cfg.shapes: a JSON string for additional shapes in the grid fields with * are required; options in  are the default values Output: flux: a struct array, with a length equals to that of cfg. For each element of flux, flux(i).data is a 4D array with dimensions specified by [size(vol) total-time-gates]. The content of the array is the normalized flux at each voxel of each time-gate. detphoton: a struct array, with a length equals to that of cfg. For each element of detphoton, detphoton(i).data is a 2D array with dimensions [size(cfg.prop,1)+1 saved-photon-num]. The first row is the ID(>0) of the detector that captures the photon; the second row saves the number of scattering events of each exiting photon; the rest rows are the partial path lengths (in grid unit) traveling in medium 1 up to the last. If you set cfg.unitinmm, you need to multiply the path-lengths to convert them to mm unit. vol: (optional) a struct array, each element is a preprocessed volume corresponding to each instance of cfg. Each volume is a 3D uint8 array. if detphoton is ignored, the detected photon will be saved in a .mch file if cfg.issavedeet=1; if no output is given, the flux will be saved to a .mc2 file if cfg.issave2pt=1 (which is true by default). Example: cfg.nphoton=1e7; cfg.vol=uint8(ones(60,60,60)); cfg.srcpos=[30 30 1]; cfg.srcdir=[0 0 1]; cfg.gpuid=1; cfg.autopilot=1; cfg.prop=[0 0 1 1;0.005 1 0 1.37]; cfg.tstart=0; cfg.tend=5e-9; cfg.tstep=5e-10; % calculate the flux distribution with the given config flux=mcxlab(cfg); cfgs(1)=cfg; cfgs(2)=cfg; cfgs(1).isreflect=0; cfgs(2).isreflect=1; cfgs(2).issavedet=1; cfgs(2).detpos=[30 20 1 1;30 40 1 1;20 30 1 1;40 30 1 1]; % calculate the flux and partial path lengths for the two configurations [fluxs,detps]=mcxlab(cfgs); imagesc(squeeze(log(fluxs(1).data(:,30,:,1)))-squeeze(log(fluxs(2).data(:,30,:,1)))); This function is part of Monte Carlo eXtreme (MCX) URL: http://mcx.sf.net License: GNU General Public License version 3, please read LICENSE.txt for details
Because MCXLAB outputs the verbose simulation information, such as intermediate timing information, to the stderr descriptor, you will not be able to see this information if you launch Matlab in the GUI mode. If you want to access to this information, please launch Matlab with the following command:
matlab -nojvm -nodesktop
The debug info will be printed in the console window. Alternatively, you can just start the matlab GUI from a console window by typing "matlab", and the debug information will be printed in the console window instead.
We provided several examples to demonstrate the basic usage of MCXLAB, as well as to perform validations of MCX algorithm using both simple homogeneous and heterogeneous domains. These examples are explained below:
In this example, we show the most basic usage of MCXLAB. This include how to define the input configuration structure, launch MCX simulations and interpret and plotting the resulting data.
In this example, we validate MCXLAB with a homogeneous medium in a cubic domain. This is exactly the example shown in Fig.5 of [Fang2009].
You can also use the alternative optical properties that has a high g value to observe the similarity between the two scattering/g configurations.
In this example, we validate the MCXLAB solver with a heterogeneous domain and the analytical solution of the diffusion model. We also demonstrate how to use sub-pixel resolution to refine the representation of heterogeneities. The domain is consisted of a 6x6x6 cm box with a 2cm diameter sphere embedded at the center.
This test is identical to that used for Fig. 3 in [Fang2010].
In this example, we demonstrate how to use sub-pixel resolution to represent the problem domain. The domain is consisted of a 6x6x6 cm box with a 2cm diameter sphere embedded at the center.
In this example, we simulate a 4-layer brain model using MCXLAB. We will investigate the differences between the solutions with and witout boundary reflections (both external and internal) and show you how to display and analyze the resulting data.
To compile MCXLAB for Matlab, you need to cd mcx/src directory, and type
from a shell window. You need to make sure your Matlab is installed and the command mex is included in your PATH environment variable. Similarly, to compile MCXLAB for Octave, you type
The command mkoctfile must be accessible from your command line and it is provided in a package named "octave3.x-headers" in Ubuntu (3.x can be 3.2 or 3.4 etc).
Screenshot for using MCXLAB in Matlab:
Screenshot for using MCXLAB in GNU Octave:
[Fang2009] Qianqian Fang and David A. Boas, "Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units," Opt. Express 17, 20178-20190 (2009)
[Fang2010] Fang Q, "Mesh-based Monte Carlo method using fast ray-tracing in Plucker coordinates," Biomed. Opt. Express 1, 165-175 (2010)