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- Author: Qianqian Fang <fangq at nmr.mgh.harvard.edu>
- License: GNU General Public License version 3 (GPLv3)
- Version: this package is part of Mesh-based Monte Carlo (MMC) 0.7.9

- 1. Introduction
- 2. Installation
- 3. How to use MMCLAB in MATLAB/Octave
- 4. Examples
- 5. How to compile MMCLAB
- 6. Screenshots
- 7. Reference

MMC is a mesh-based Monte Carlo (MC) photon simulation software. It can use a tetrahedral mesh to model complex anatomical structures, thus, is considered to be more accurate and computationally efficient than the conventional MC codes.

MMCLAB is the native MEX version of MMC for MATLAB and GNU Octave. By converting the input and output files into convenient in-memory variables, MMCLAB is very intuitive to use and straightforward to be integrated with mesh-generation and post-simulation analyses.

Because MMCLAB contains the same computational codes for multi-threading photon simulation as in a MMC binary, running MMCLAB inside MATLAB is expected to give similar speed as running a standalone MMC binary.

Installation of MMCLAB is very straightforward. You first download and unzip the MMCLAB package into a folder; then you add the folder path into MATLAB's search path list. This can be done with the "addpath" command in a working session; if you want to add this path permanently, use the "pathtool" command, or edit your startup.m (~/.octaverc for Octave).

After installation, please type "help mmclab" in MATLAB/Octave to print the help information.

To learn the basic usage of MMCLAB, you can type

help mmclab

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 MMC.

==================================================================== MMCLAB - Monte Carlo eXtreme (MMC) for MATLAB/GNU Octave -------------------------------------------------------------------- Copyright (c) 2010,2011 Qianqian Fang <fangq at nmr.mgh.harvard.edu> URL: http://mcx.sf.net ==================================================================== Format: [flux,detphoton]=mmclab(cfg,type); Input: cfg: a struct, or struct array. Each element in cfg defines a set of parameters for a simulation. It may contain the following fields: *cfg.nphoton: the total number of photons to be simulated (integer) *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.node: node array for the input tetrahedral mesh, 3 columns: (x,y,z) *cfg.elem: element array for the input tetrahedral mesh, 4 columns *cfg.elemprop: element property index for input tetrahedral mesh *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.seed: seed for the random number generator (integer) [0] cfg.detpos: an N by 4 array, each row specifying a detector: [x,y,z,radius] cfg.isreflect: [1]-consider refractive index mismatch, 0-matched index cfg.isnormalized:[1]-normalize the output flux to unitary source, 0-no reflection cfg.isspecular: [1]-calculate specular reflection if source is outside cfg.basisorder: [1]-linear basis, 0-piece-wise constant basis cfg.outputformat:['ascii'] or 'bin' (in 'double') cfg.outputtype: [X] - output flux, F - fluence, E - energy deposit cfg.method: ray-tracing method, [P]:Plucker, H:Havel (SSE4), B: partial Badouel, S: branchless Badouel (SSE) cfg.debuglevel: debug flag string, a subset of [MCBWDIOXATRPE] cfg.nout: [1.0] refractive index for medium type 0 (background) cfg.minenergy: terminate photon when weight less than this level (float) [0.0] cfg.roulettesize:[10] size of Russian roulette cfg.e0: element that encloses the source (calc. if missing) cfg.srctype: source type, can be ["pencil"],"isotropic" or "cone" cfg.srcparam: 1x4 vector for additional source parameter cfg.unitinmm: defines the unit in the input mesh [1.0] fields with * are required; options in [] are the default values type: omit or 'omp' for multi-threading version; 'sse' for SSE4 version the SSE4 version is about 25% faster, but requires newer CPUs. Output: flux: a struct array, with a length equals to that of cfg. For each element of flux, flux(i).data is a 1D vector with dimensions [size(cfg.node,1) total-time-gates] if cfg.basisorder=1, or [size(cfg.elem,1) total-time-gates] if cfg.basisorder=0. The content of the array is the normalized flux (or others depending on cfg.outputtype) at each mesh node and 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 is the number of scattering events of the exitting 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. Example: cfg.nphoton=1e6; [cfg.node face cfg.elem]=meshabox([0 0 0],[60 60 30],6); cfg.elemprop=ones(size(cfg.elem,1),1); cfg.srcpos=[30 30 0]; cfg.srcdir=[0 0 1]; cfg.prop=[0 0 1 1;0.005 1 0 1.37]; cfg.tstart=0; cfg.tend=5e-9; cfg.tstep=5e-10; cfg.debuglevel='P'; % calculate the flux distribution with the given config flux=mmclab(cfg); cfgs(1)=cfg; cfgs(2)=cfg; cfgs(1).isreflect=0; cfgs(2).isreflect=1; cfgs(2).detpos=[30 20 0 1;30 40 0 1;20 30 1 1;40 30 0 1]; % calculate the flux and partial path lengths for the two configurations [fluxs,detps]=mmclab(cfgs); This function is part of Mesh-based Monte Carlo (MMC) URL: http://mcx.sf.net/mmc License: GNU General Public License version 3, please read LICENSE.txt for details

We provided several examples to demonstrate the basic usage of MMCLAB, as well as to perform validations of MMC algorithm using both simple homogeneous and heterogeneous domains. These examples are explained below:

In this example, we show the most basic usage of MMCLAB. This include how to define the input configuration structure, launch MMC simulations and plot the results.

To compile MMCLAB for MATLAB, you need to cd mmc/src directory, and type

make mex

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 MMCLAB for Octave, you type

make oct

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 MMCLAB in MATLAB:

<to be added>

Screenshot for using MMCLAB in GNU Octave:

<to be added>

[Fang2010] Fang Q, "Mesh-based Monte Carlo method using fast ray-tracing in Plucker coordinates," Biomed. Opt. Express 1, 165-175 (2010)