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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) 0.9.5-1
- URL: http://mcx.sf.net/cgi-bin/index.cgi?MMC/Doc/MMCLAB

- 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 utilize a tetrahedral mesh to model a complex anatomical structure, thus, has been shown 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. If your CPU supports, running mmclab with an 'sse' option can be 25% faster than the standard mode.

MMCLAB can be downloaded using the general download link for MMC:

http://mcx.sourceforge.net/cgi-bin/index.cgi?MMC/Download

Installation of MMCLAB is straightforward. You first download the MMCLAB package and unzip it to 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 - Mesh-based Monte Carlo (MMC) for MATLAB/GNU Octave -------------------------------------------------------------------- Copyright (c) 2012,2014 Qianqian Fang <fangq at nmr.mgh.harvard.edu> URL: http://mcx.sf.net/mmc/ ==================================================================== Format: [flux,detphoton,ncfg,seeds]=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.facenb: element face neighbohood list (calculated by faceneighbors()) -cfg.evol: element volume (calculated by elemvolume() with iso2mesh) -cfg.e0: the element ID enclosing the source, if not defined, it will be calculated by tsearchn(node,elem,srcpos); if cfg.e0 is set as one of the following characters, mmclab will do an initial ray-tracing and move srcpos to the first intersection to the surface: '>': search along the forward (srcdir) direction '<': search along the backward direction '-': search both directions cfg.seed: seed for the random number generator (integer) [0] if set as a uint8 array, the binary data in each column is used to seed a photon (i.e. the "replay" mode) 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.ismomentum: [0]-save momentum transfer for each detected photon cfg.issaveexit: [0]-save the position (x,y,z) and (vx,vy,vz) for a detected photon cfg.issaveseed: [0]-save the RNG seed for a detected photon so one can replay 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 J - Jacobian (replay), T - approximated Jacobian (replay mode) 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], no space 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.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 marked with * are required; options in [] are the default values fields marked with - are calculated if not given (can be faster if precomputed) type: omit or 'omp' for multi-threading version; 'sse' for the SSE4 MMC, the SSE4 version is about 25% faster, but requires newer CPUs; if type='prep' with a single output, mmclab returns ncfg only. 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: (optional) 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. ncfg: (optional), if given, mmclab returns the preprocessed cfg structure, including the calculated subfields (marked by "-"). This can be used as the input to avoid repetitive preprocessing. seeds: (optional), if give, mmclab returns the seeds, in the form of a byte array (uint8) for each detected photon. The column number of seed equals that of detphoton. Example: cfg.nphoton=1e5; [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='TP'; % populate the missing fields to save computation ncfg=mmclab(cfg,'prep'); cfgs(1)=ncfg; cfgs(2)=ncfg; 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; some of the examples also perform validations of MMC algorithm in both homogeneous and heterogeneous domains. These examples are explained below:

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

In this example, we validate MMCLAB with a homogeneous medium in a cubic domain. This is the same as in mmc/examples/validation; the details of the simulation is described in Fig.2 of [Fang2010].

In this example, we validate the MMCLAB solver with a heterogeneous domain and the analytical solution from the diffusion model. The domain is consisted of a 6x6x6 cm box with a 2cm diameter sphere embedded at the center.

This test is identical to the simulations under mmc/examples/meshtest, which are described by Fig. 3 in [Fang2010].

In this example, we demonstrate how to use the debug flags to print photon trajectories. The domain is a simple 10x10x10mm cube.

This test is similar to the simulations under mmc/examples/onecube.

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

make mexor

make mexsse

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 octor

make octsse

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:

Screenshot for using MMCLAB in GNU Octave:

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