- Author: Qianqian Fang (q.fang at neu.edu)
- License: GNU General Public License version 3 (GPLv3)
- Version: 2.6.pre (v2024.6, Jumbo Jolt)
- Website: https://mcx.space
- Download: https://mcx.space/wiki/?Get
Table of Content:
- What's New
- Introduction
- Requirement and Installation
- Running Simulations
- Using JSON-formatted input files
- Using JSON-formatted shape description files
- Output data formats
- Using MCXLAB in MATLAB and Octave
- Using MCX Studio GUI
- Interpreting the Output
- Best practices guide
- Acknowledgement
- Reference
MCX v2024.2 contains both major new features and critical bug fixes. It is a strongly recommended upgrade for all users.
Specifically, MCX v2024.2 received three important new features:
- user-defined photon launch angle distribution (fangq#13)
- simulate multiple sources in a single session (fangq#163)
- per-voxel mua/mus/g/n format support (fangq#203)
Similar to the user-defined phase-function feature included in
MCX v2023, the first feature allows users to define the zenith angle distribution for
launching a photon, relative to the source-direction vector, also via
a discretized inverse CDF (cumulative distribution function).
In MATLAB/Octave, they can be set as cfg.invcdf or cfg.angleinvcdf,
respectively. We provided ready-to-use demo scripts in
mcxlab/examples/demo_mcxlab_phasefun.m and demo_mcxlab_launchangle.m.
The second feature allow users to specify more then one sources of the
same type when defining srcpos, srcdir, srcparam1 and srcparam2.
Each source can have independent weight controled by the 4th element of srcpos.
Aside from new features, a severe bug was discovered that affects all
pattern and pattern3d simulations that do not use photon-sharing,
please see
Because of this bug, MCX/MCX-CL in older releases has been simulating squared pattern/pattern3d data instead of the pattern itself. If your simulation uses these two source types and you do not use photon-sharing (i.e. simulating multiple patterns together), please upgrade your MCX/MCX-CL and rerun your simulations. This bug only affect volumetric fluence/flux outputs; it does not affect diffuse reflectance outputs or binary patterns.
It also includes a bug fix from fangq#195 regarding precision loss when saving diffuse reflectance.
We want to thank Haohui Zhang for reporting a number of critical issues (fangq#195 and fangq#212), Liam Keegan for contributing a patch to extend the slit source (fangq#214). ShijieYan and fanyuyen have also contributed to the bug fixes and improvements.
The detailed updates can be found in the below change log
- 2024-03-13 [abdee14] [bug] fix multi-source replay bug, close #215
- 2024-03-11 [9250a0d] [doc] make final doc update, bump pmcx to v0.3.2
- 2024-03-10 [4e7f404] [ci] revert to windows-2019, add help info for #214, add note on nvidia-uvm
- 2024-03-10 [2750d70] [ci] choco install is failing on Windows, see actions/runner-images#9477
- 2024-03-10 [7b6e0e0] [optimize] cut hyperboloid gaussian register use from 15 to 3, #127,#214
- 2024-03-10 [a2279d6] [optimize] reduce gaussian slit register use from 9 to 2, #214
- 2024-03-08 [4708e10] [doc] Create pull_request_template.md
- 2024-03-08 [14ca45d] [feat] use 'make register' to report register counts in the Makefile
- 2024-03-07 [411a007] [feat] add gaussian broadening to slit source (contributed by Liam Keegan, #214)
- 2024-03-04 [1e6c403] [doc] update instructions on running mcx on hybrid GPU Linux laptops
- 2024-03-04 [0344d84] [bug] fix cuda core count for Ada and Blackwell
- 2024-02-27 [558dbab] [pmcx] bump pmcx to 0.2.12 after fixing critical bug #212
- 2024-02-27 [8e03878] *[bug] critical: fix double-multiplication of pattern launched weight, fix #212
- 2024-02-23 [61ae0b8] [bug] fix detid value #209
- 2024-02-23 [90e2419] [bug] fix pmcx dettpsf overwrites input bug
- 2024-02-21 [744fba2] [bug] update srcparam2 after volume preprocessing close #206
- 2024-02-18 [dbb23be] [ci] test removing lazhelphtml.pas to avoid error on linux/macos runners
- 2024-02-18 [4c88de2] [ci] test fix for lazarus 3.0 build error on macos action
- 2024-02-18 [54f732e] [mcxcloud] support backup servers, support x/y/z slice view and time-gates
- 2024-02-16 [8a8ff90] [ci] install libomp before downgrade xcode
- 2024-02-16 [99ca38f] [ci] test libomp error on macos runners
- 2024-02-13 [cb9b6c2] [doc] update openjdata to neurojson
- 2024-02-04 [007ecce] [feat] accept 3-element srcparam1/srcparam2 in mcxlab/pmcx
- 2024-01-23 [20b0aa2] [minor] debug addlog callback error
- 2024-01-23 [f63f73b] [bug] fix chrome webpage error messages
- 2024-01-11 [73fe834] [mcxcloud] add the missing flag to show optional json fields
- 2024-01-01 [0eb1a48] [doc] update changelog
- 2024-01-01 [4232764] [mcxlab] add speed comparison between different media formats
- 2024-01-01 [3bca606] [minor] fix typo
- 2024-01-01 [51003bc] *[format] automatic format all matlab script with miss_hit
- 2024-01-01 [9951dd8] [minor] update copyright year
- 2024-01-01 [0f48b8f] [format] update source unit header info
- 2024-01-01 [6707eaa] [bug] fix detp output bug after adding srcid, #163
- 2024-01-01 [49b5cef] [ci] update lazarus on windows to avoid build crash
- 2024-01-01 [9dc832d] *[feat] support --srcid to simulate all, one or separate sources, #163
- 2023-12-31 [f5b4aaa] [minor] fix file name spelling
- 2023-12-30 [7e1aec0] *[feat] simulate multiple sources, close #163
- 2023-12-29 [59f18a7] *[feat] complete per-voxel mua/mus/g/n format support, close #203
- 2023-12-29 [1e3b031] [feat] initial implementation of mua/mus/g/n all float format
- 2023-12-29 [1100e36] [bug] fix mcx2json bug when exporting 4D vol, fix #200
- 2023-12-17 [9831c7e] [debug] print jdata compression message inside matlab
- 2023-11-27 [28e5101] [pmcx] bump pmcx version to 0.2.7
- 2023-11-27 [fae5b72] [pmcx] typecast traj.id from float to uint32, fix #199
- 2023-11-16 [4eec905] [mcxlab] add demo script comparing conv vs direct area src
- 2023-11-10 [6771752] [ci] fix matlab mex error after mingw64 was removed, matlab-actions/setup-matlab#75
- 2023-11-08 [f4aec48] [ci] test conda command to install octave on mac
- 2023-11-08 [c976cbf] [ci] brew refuses to install octave, switch to conda
- 2023-11-07 [325a522] [release] post v2023 release action
- 2023-11-07 [a710ab3] allow converting integer cfg.vol to json
- 2023-10-31 [d6c64e4] [test] fix rng test after make double
- 2023-10-30 [08361eb] [pmcx] bump pmcx to v0.2.6 with dref fix #195
- 2023-10-30 [9220578] *[bug] apply #41 like 2xfloat-buffer for dref accumulation, fix #195
- 2023-10-24 [961d059] pattern json data rstrict to single,set show_opt_in option
- 2023-10-24 [5f130fc] use 2d pattern by default
- 2023-10-24 [db608f7] use jq to format json schema; add Source.Pattern in schema
- 2023-10-14 [4c365f9] update zh-cn translation
- 2023-10-12 [8d23726] fix windows ci error
- 2023-10-12 [2904cc3] avoid error when compressing binaries
- 2023-10-12 [2ebe3de] include language locale files in github CI build
- 2023-10-12 [5782cff] enable DefaultTranslator for i18n support in mcxstudio
- 2023-10-11 [833d117] add compiled mo locale file
- 2023-10-11 [ad298c1] add initial translation for simplified Chinese
- 2023-10-11 [aa15780] add strings from additional forms
- 2023-10-10 [14285a0] *prepare for adding i18n support
- 2023-10-10 [c5496ac] *force invcdf/angleinvcdf even count of float, reapply 53d7ac0, fix #193
- 2023-10-09 [ca1bf2b] use focal-length to select interpolation or discrete mode, #129
- 2023-10-08 [be8b8c3] *support user-defined launch angle profile, fix #129
- 2023-10-03 [2ad9307] update winget package files to the v2023 release
- 2023-10-03 [26ede84] rename winget package file
- 2023-10-03 [2e71a51] treat cfg->bc as a null-ending string, #191 #192
- 2023-10-03 [68db492] make cfg->bc a null terminated string
- 2023-10-02 [35170f9] Merge pull request #192 from lkeegan/pmcx_bc_overflow_error
- 2023-10-02 [3c77170] Fix buffer overflow error when bc has 12 characters
Monte Carlo eXtreme (MCX) is a fast physically-accurate photon simulation software for 3D heterogeneous complex media. By taking advantage of the massively parallel threads and extremely low memory latency in a modern graphics processing unit (GPU), this program is able to perform Monte Carlo (MC) simulations at a blazing speed, typically hundreds to a thousand times faster than a single-threaded CPU-based MC implementation.
MCX is written in C and NVIDIA CUDA. It only be executed on NVIDIA GPUs. If you want to run hardware-accelerated MCX simulations on AMD/Intel GPUs or CPUs, please download MCX-CL (MCX for OpenCL), which is written in OpenCL. MCX and MCX-CL are highly compatible.
Due to the nature of the underlying MC algorithms, MCX and MCX-CL are ray-tracing/ray-casting software under-the-hood. Compared to commonly seen ray-tracing libraries used in computer graphics or gaming engines, MCX-CL and MCX have many unique characteristics. The most important difference is that MCX/MCX-CL are rigorously based on physical laws. They are numerical solvers to the underlying radiative transfer equation (RTE) and their solutions have been validated across many publications using optical instruments and experimental measurements. In comparison, most graphics-oriented ray-tracers have to make many approximations in order to achieve fast rendering, enable to provide quantitatively accurate light simulation results. Because of this, MCX/MCX-CL have been extensively used by biophotonics research communities to obtain reference solutions and guide the development of novel medical imaging systems or clinical applications. Additionally, MCX/MCX-CL are volumetric ray-tracers; they traverse photon-rays throughout complex 3-D domains and computes physically meaningful quantities such as spatially resolved fluence, flux, diffuse reflectance/transmittance, energy deposition, partial pathlengths, among many others. In contrast, most graphics ray-tracing engines only trace the RGB color of a ray and render it on a flat 2-D screen. In other words, MCX/MCX-CL gives physically accurate 3-D light distributions while graphics ray-tracers focus on 2-D rendering of a scene at the camera. Nonetheless, they share many similarities, such as ray-marching computation, GPU acceleration, scattering/absorption handling etc.
The algorithm of this software is detailed in the References [Fang2009,Yu2018,Yan2020]. A short summary of the main features includes:
- 3D heterogeneous media represented by voxelated array
- support complex sources including wide-field and pattern illuminations
- boundary reflection support
- time-resolved photon transport simulations
- saving photon partial path lengths and trajectories
- optimized random number generators
- build-in flux/fluence normalization to output Green's functions
- user adjustable voxel resolution
- improved accuracy with atomic operations
- cross-platform graphical user interface
- native Matlab/Octave support for high usability
- flexible JSON interface for future extensions
- multi-GPU support
- advanced features: photon-replay, photon-sharing, and more
This software can be used on Windows, Linux and Mac OS. MCX is written in C/CUDA and requires NVIDIA GPUs (support for AMD/Intel CPUs/GPUs via ROCm is still under development). A more portable OpenCL implementation of MCX, i.e. MCXCL, was announced on July, 2012 and supports almost all NVIDIA/AMD/Intel CPU and GPU models. If your hardware does not support CUDA, please download MCXCL from the below URL:
https://mcx.space/wiki/index.cgi?Learn#mcxcl
Please read this section carefully. The majority of failures using MCX were found related to incorrect installation of NVIDIA GPU driver.
Please browse https://mcx.space/#documentation for step-by-step instructions.
For MCX-CUDA, the requirements for using this software include
- a CUDA capable NVIDIA graphics card
- pre-installed NVIDIA graphics driver
You must make sure that your NVIDIA graphics driver was installed properly.
A list of CUDA capable cards can be found at [2]. The oldest
GPU architecture that MCX source code can be compiled is Fermi (sm_20).
Using the latest NVIDIA card is expected to produce the best
speed. The officially released binaries (including mex files and pmcx modules)
can run on NVIDIA GPUs as old as Kepler (GTX-730, sm_35). All MCX binaries
can run directly on future generations of NVIDIA GPUs without needing to
be recompiled, therefore forward-compatible.
In the below webpage, we summarized the speed differences between different generations of NVIDIA GPUs
For simulations with large volumes, sufficient graphics memory is also required to perform the simulation. The minimum amount of graphics memory required for a MC simulation is Nx*Ny*Nz bytes for the input tissue data plus Nx*Ny*Nz*Ng*4*2 bytes for the output flux/fluence data - where Nx,Ny,Nz are the dimensions of the tissue volume, Ng is the number of concurrent time gates, 4 is the size of a single-precision floating-point number, 2 is for the extra memory needed to ensure output accuracy (fangq#41). MCX does not require double-precision support in your hardware.
MCX stores optical properties and detector positions in the constant memory. Usually, NVIDIA GPUs provides about 64 kB constant memory. As a result, we can only the total number of optical properties plus the number of detectors can not exceed 4000 (4000 * 4 * 4 = 64 k).
In addition, MCX stores detected photon data inside the shared memory, which also ranges between 42 kB to 100 kB per stream processor across different GPU generations. If your domain contains many medium types, it is possible that the allocation of the shared memory can exceed the limit. You will also receive an "out of memory" error.
To install MCX, you need to download the binary executable compiled for your
computer architecture (32 or 64bit) and platform, extract the package and run
the executable under the {mcx root}/bin directory.
For Windows users, you must make sure you have installed the appropriate NVIDIA
driver for your GPU. You should also configure your OS to run CUDA simulations.
This requires you to open the mcx/setup/win64 folder using your file explorer,
right-click on the apply_timeout_registry_fix.bat file and select
“Run as administrator”. After confirmation, you should see a windows
command window with message
Patching your registry
Done
Press any key to continue ...
You MUST REBOOT your Windows computer to make this setting effective. The above patch modifies your driver settings so that you can run MCX simulations for longer than a few seconds. Otherwise, when running MCX for over a few seconds, you will get a CUDA error: “unspecified error”.
Please see the below link for details
https://mcx.space/wiki/index.cgi?Doc/FAQ#I_am_getting_a_kernel_launch_timed_out_error_what_is_that
If you use Linux, you may enable Intel integrated GPU (iGPU) for display while
leaving your NVIDIA GPU dedicated for computing using nvidia-prime, see
or choose one of the 4 other approaches in this blog post
https://nvidia.custhelp.com/app/answers/detail/a_id/3029/~/using-cuda-and-x
We noticed that running Ubuntu Linux 22.04 with a 6.5 kernel on a laptop with a hybrid GPU with an Intel iGPU and an NVIDIA GPU, you must configure the laptop to use the NVIDIA GPU as the primary GPU by choosing "NVIDIA (Performance Mode)" in the PRIME Profiles section of NVIDIA X Server Settings. You can also run
sudo prime-select nvidia
to achieve the same goal. Otherwise, the simulation may hang your system after running for a few seconds. A hybrid GPU laptop combing an NVIDIA GPU with an AMD iGPU does not seem to have this issue if using Linux.
In addition, NVIDIA drirver (520 or newer) has a known glitch running on Linux kernel 6.x (such as those in Ubuntu 22.04). See
When the laptop is in the "performance" mode and wakes up from suspension, MCX or any CUDA program fails to run with an error
MCX ERROR(-999):unknown error in unit mcx_core.cu:2523
This is because the kernel module nvida-uvm fails to be reloaded after suspension.
If you had an open MATLAB session, you must close MATLAB first, and
run the below commands (if MATLAB is open, you will see rmmod: ERROR: Module nvidia_uvm is in use)
sudo rmmod /dev/nvidia-uvm
sudo modprobe nvidia-uvm
after the above command, MCX should be able to run again.
New generations of Mac computers no longer support NVIDIA or AMD GPUs. you will have to use the OpenCL version of MCX, MCX-CL by downloading it from
https://mcx.space/wiki/?Learn#mcxcl
To run a simulation, the minimum input is a configuration (text) file, and, if
the input file does not contain built-in domain shape descriptions, an external
volume file (a binary file with a specified voxel format via -K/--mediabyte).
Typing mcx without any parameters prints the help information and a list of
supported parameters, as shown below:
###############################################################################
# Monte Carlo eXtreme (MCX) -- CUDA #
# Copyright (c) 2009-2024 Qianqian Fang <q.fang at neu.edu> #
# https://mcx.space/ & https://neurojson.io/ #
# #
# Computational Optics & Translational Imaging (COTI) Lab- http://fanglab.org #
# Department of Bioengineering, Northeastern University, Boston, MA, USA #
###############################################################################
# The MCX Project is funded by the NIH/NIGMS under grant R01-GM114365 #
###############################################################################
# Open-source codes and reusable scientific data are essential for research, #
# MCX proudly developed human-readable JSON-based data formats for easy reuse.#
# #
#Please visit our free scientific data sharing portal at https://neurojson.io/#
# and consider sharing your public datasets in standardized JSON/JData format #
###############################################################################
$Rev::d593a0$v2024.2 $Date::2024-03-04 00:04:10 -05$ by $Author::Qianqian Fang$
###############################################################################
usage: mcx <param1> <param2> ...
where possible parameters include (the first value in [*|*] is the default)
== Required option ==
-f config (--input) read an input file in .json or .inp format
if the string starts with '{', it is parsed as
an inline JSON input file
or
--bench ['cube60','skinvessel',..] run a buint-in benchmark specified by name
run --bench without parameter to get a list
== MC options ==
-n [0|int] (--photon) total photon number (exponential form accepted)
max accepted value:9.2234e+18 on 64bit systems
-r [1|+/-int] (--repeat) if positive, repeat by r times,total= #photon*r
if negative, divide #photon into r subsets
-b [1|0] (--reflect) 1 to reflect photons at ext. boundary;0 to exit
-B '______' (--bc) per-face boundary condition (BC), 6 letters for
/case insensitive/ bounding box faces at -x,-y,-z,+x,+y,+z axes;
overwrite -b if given.
each letter can be one of the following:
'_': undefined, fallback to -b
'r': like -b 1, Fresnel reflection BC
'a': like -b 0, total absorption BC
'm': mirror or total reflection BC
'c': cyclic BC, enter from opposite face
if input contains additional 6 letters,
the 7th-12th letters can be:
'0': do not use this face to detect photon, or
'1': use this face for photon detection (-d 1)
the order of the faces for letters 7-12 is
the same as the first 6 letters
eg: --bc ______010 saves photons exiting at y=0
-u [1.|float] (--unitinmm) defines the length unit for the grid edge
-U [1|0] (--normalize) 1 to normalize flux to unitary; 0 save raw
-E [1648335518|int|mch](--seed) set rand-number-generator seed, -1 to generate
if an mch file is followed, MCX "replays"
the detected photon; the replay mode can be used
to calculate the mua/mus Jacobian matrices
-z [0|1] (--srcfrom0) 1 volume origin is [0 0 0]; 0: origin at [1 1 1]
-k [1|0] (--voidtime) when src is outside, 1 enables timer inside void
-Y [0|int] (--replaydet) replay only the detected photons from a given
detector (det ID starts from 1), used with -E
if 0, replay all detectors and sum all Jacobians
if -1, replay all detectors and save separately
-V [0|1] (--specular) 1 source located in the background,0 inside mesh
-e [0.|float] (--minenergy) minimum energy level to trigger Russian roulette
-g [1|int] (--gategroup) number of maximum time gates per run
== GPU options ==
-L (--listgpu) print GPU information only
-t [16384|int](--thread) total thread number
-T [64|int] (--blocksize) thread number per block
-A [1|int] (--autopilot) 1 let mcx decide thread/block size, 0 use -T/-t
-G [0|int] (--gpu) specify which GPU to use, list GPU by -L; 0 auto
or
-G '1101' (--gpu) using multiple devices (1 enable, 0 disable)
-W '50,30,20' (--workload) workload for active devices; normalized by sum
-I (--printgpu) print GPU information and run program
--atomic [1|0] 1: use atomic operations to avoid thread racing
0: do not use atomic operation (not recommended)
== Input options ==
-P '{...}' (--shapes) a JSON string for additional shapes in the grid.
only the root object named 'Shapes' is parsed
and added to the existing domain defined via -f
or --bench
-j '{...}' (--json) a JSON string for modifying all input settings.
this input can be used to modify all existing
settings defined by -f or --bench
-K [1|int|str](--mediabyte) volume data format, use either a number or a str
voxel binary data layouts are shown in {...}, where [] for byte,[i:]
for 4-byte integer, [s:] for 2-byte short, [h:] for 2-byte half float,
[f:] for 4-byte float; on Little-Endian systems, least-sig. bit on left
1 or byte: 0-128 tissue labels
2 or short: 0-65535 (max to 4000) tissue labels
4 or integer: integer tissue labels
96 or asgn_float: mua/mus/g/n 4xfloat format
{[f:mua][f:mus][f:g][f:n]}
97 or svmc: split-voxel MC 8-byte format
{[n.z][n.y][n.x][p.z][p.y][p.x][upper][lower]}
98 or mixlabel: label1+label2+label1_percentage
{[label1][label2][s:0-32767 label1 percentage]}
99 or labelplus: 32bit composite voxel format
{[h:mua/mus/g/n][s:(B15-16:0/1/2/3)(label)]}
100 or muamus_float: 2x 32bit floats for mua/mus
{[f:mua][f:mus]}; g/n from medium type 1
101 or mua_float: 1 float per voxel for mua
{[f:mua]}; mus/g/n from medium type 1
102 or muamus_half: 2x 16bit float for mua/mus
{[h:mua][h:mus]}; g/n from medium type 1
103 or asgn_byte: 4x byte gray-levels for mua/s/g/n
{[mua][mus][g][n]}; 0-255 mixing prop types 1&2
104 or muamus_short: 2x short gray-levels for mua/s
{[s:mua][s:mus]}; 0-65535 mixing prop types 1&2
when formats 99 or 102 is used, the mua/mus values in the input volume
binary data must be pre-scaled by voxel size (unitinmm) if it is not 1.
pre-scaling is not needed when using these 2 formats in mcxlab/pmcx
-a [0|1] (--array) 1 for C array (row-major); 0 for Matlab array
== Output options ==
-s sessionid (--session) a string to label all output file names
-O [X|XFEJPMRL](--outputtype) X - output flux, F - fluence, E - energy deposit
/case insensitive/ J - Jacobian (replay mode), P - scattering,
event counts at each voxel (replay mode only)
M - momentum transfer; R - RF/FD Jacobian
L - total pathlength
-d [1|0-3] (--savedet) 1 to save photon info at detectors; 0 not save
2 reserved, 3 terminate simulation when detected
photon buffer is filled
-w [DP|DSPMXVW](--savedetflag)a string controlling detected photon data fields
/case insensitive/ 1 D output detector ID (1)
2 S output partial scat. even counts (#media)
4 P output partial path-lengths (#media)
8 M output momentum transfer (#media)
16 X output exit position (3)
32 V output exit direction (3)
64 W output initial weight (1)
combine multiple items by using a string, or add selected numbers together
by default, mcx only saves detector ID and partial-path data
-x [0|1] (--saveexit) 1 to save photon exit positions and directions
setting -x to 1 also implies setting '-d' to 1.
same as adding 'XV' to -w.
-X [0|1] (--saveref) 1 to save diffuse reflectance at the air-voxels
right outside of the domain; if non-zero voxels
appear at the boundary, pad 0s before using -X
-m [0|1] (--momentum) 1 to save photon momentum transfer,0 not to save.
same as adding 'M' to the -w flag
-q [0|1] (--saveseed) 1 to save photon RNG seed for replay; 0 not save
-M [0|1] (--dumpmask) 1 to dump detector volume masks; 0 do not save
-H [1000000] (--maxdetphoton) max number of detected photons
-S [1|0] (--save2pt) 1 to save the flux field; 0 do not save
-F [jnii|...](--outputformat) fluence data output format:
mc2 - MCX mc2 format (binary 32bit float)
jnii - JNIfTI format (https://neurojson.org)
bnii - Binary JNIfTI (https://neurojson.org)
nii - NIfTI format
hdr - Analyze 7.5 hdr/img format
tx3 - GL texture data for rendering (GL_RGBA32F)
the bnii/jnii formats support compression (-Z) and generate small files
load jnii (JSON) and bnii (UBJSON) files using below lightweight libs:
MATLAB/Octave: JNIfTI toolbox https://neurojson.org/download/jnifti
MATLAB/Octave: JSONLab toolbox https://neurojson.org/download/jsonlab
Python: PyJData: https://neurojson.org/download/pyjdata
JavaScript: JSData: https://neurojson.org/download/jsdata
-Z [zlib|...] (--zip) set compression method if -F jnii or --dumpjson
is used (when saving data to JSON/JNIfTI format)
0 zlib: zip format (moderate compression,fast)
1 gzip: gzip format (compatible with *.gz)
2 base64: base64 encoding with no compression
3 lzip: lzip format (high compression,very slow)
4 lzma: lzma format (high compression,very slow)
5 lz4: LZ4 format (low compression,extrem. fast)
6 lz4hc: LZ4HC format (moderate compression,fast)
--dumpjson [-,0,1,'file.json'] export all settings, including volume data using
JSON/JData (https://neurojson.org) format for
easy sharing; can be reused using -f
if followed by nothing or '-', mcx will print
the JSON to the console; write to a file if file
name is specified; by default, prints settings
after pre-processing; '--dumpjson 2' prints
raw inputs before pre-processing
== User IO options ==
-h (--help) print this message
-v (--version) print MCX revision number
-l (--log) print messages to a log file instead
-i (--interactive) interactive mode
== Debug options ==
-D [0|int] (--debug) print debug information (you can use an integer
or or a string by combining the following flags)
-D [''|RMPT] 1 R debug RNG
/case insensitive/ 2 M store photon trajectory info
4 P print progress bar
8 T save trajectory data only, disable flux/detp
combine multiple items by using a string, or add selected numbers together
== Additional options ==
--root [''|string] full path to the folder storing the input files
--gscatter [1e9|int] after a photon completes the specified number of
scattering events, mcx then ignores anisotropy g
and only performs isotropic scattering for speed
--srcid [0|-1,0,1,2,..] -1 simulate multi-source separately;0 all sources
together; a positive integer runs a single source
--internalsrc [0|1] set to 1 to skip entry search to speedup launch
--maxvoidstep [1000|int] maximum distance (in voxel unit) of a photon that
can travel before entering the domain, if
launched outside (i.e. a widefield source)
--maxjumpdebug [10000000|int] when trajectory is requested (i.e. -D M),
use this parameter to set the maximum positions
stored (default: 1e7)
== Example ==
example: (list built-in benchmarks)
mcx --bench
or (list supported GPUs on the system)
mcx -L
or (use multiple devices - 1st,2nd and 4th GPUs - together with equal load)
mcx --bench cube60b -n 1e7 -G 1101 -W 10,10,10
or (use inline domain definition)
mcx -f input.json -P '{"Shapes":[{"ZLayers":[[1,10,1],[11,30,2],[31,60,3]]}]}'
or (use inline json setting modifier)
mcx -f input.json -j '{"Optode":{"Source":{"Type":"isotropic"}}}'
or (dump simulation in a single json file)
mcx --bench cube60planar --dumpjson
To further illustrate the command line options, below one can find a sample command
mcx -A 0 -t 16384 -T 64 -n 1e7 -G 1 -f input.json -r 2 -s test -g 10 -d 1 -w dpx -b 1
the command above asks mcx to manually (-A 0) set GPU threads, and launch 16384
GPU threads (-t) with every 64 threads a block (-T); a total of 1e7 photons (-n)
are simulated by the first GPU (-G 1) and repeat twice (-r) - i.e. total 2e7 photons;
the media/source configuration will be read from a JSON file named input.json
(-f) and the output will be labeled with the session id “test” (-s); the
simulation will run 10 concurrent time gates (-g) if the GPU memory can not
simulate all desired time gates at once. Photons passing through the defined
detector positions are saved for later rescaling (-d); refractive index
mismatch is considered at media boundaries (-b).
Historically, MCX supports an extended version of the input file format (.inp) used by tMCimg. However, we are phasing out the .inp support and strongly encourage users to adopt JSON formatted (.json) input files. Many of the advanced MCX options are only supported in the JSON input format.
A legacy .inp MCX input file looks like this:
1000000 # total photon, use -n to overwrite in the command line
29012392 # RNG seed, negative to generate, use -E to overwrite
30.0 30.0 0.0 1 # source position (in grid unit), the last num (optional) sets --srcfrom0 (-z)
0 0 1 0 # initial directional vector, 4th number is the focal-length, 0 for collimated beam, nan for isotropic
0.e+00 1.e-09 1.e-10 # time-gates(s): start, end, step
semi60x60x60.bin # volume ('unsigned char' binary format, or specified by -K/--mediabyte)
1 60 1 60 # x voxel size in mm (isotropic only), dim, start/end indices
1 60 1 60 # y voxel size, must be same as x, dim, start/end indices
1 60 1 60 # y voxel size, must be same as x, dim, start/end indices
1 # num of media
1.010101 0.01 0.005 1.37 # scat. mus (1/mm), g, mua (1/mm), n
4 1.0 # detector number and default radius (in grid unit)
30.0 20.0 0.0 2.0 # detector 1 position (real numbers in grid unit) and individual radius (optional)
30.0 40.0 0.0 # ..., if individual radius is ignored, MCX will use the default radius
20.0 30.0 0.0 #
40.0 30.0 0.0 #
pencil # source type (optional)
0 0 0 0 # parameters (4 floats) for the selected source
0 0 0 0 # additional source parameters
Note that the scattering coefficient mus=musp/(1-g).
The volume file (semi60x60x60.bin in the above example), can be read in two
ways by MCX: row-major[3] or column-major depending on the value of the user
parameter -a. If the volume file was saved using matlab or fortran, the
byte order is column-major, and you should use -a 0 or leave it out of
the command line. If it was saved using the fwrite() in C, the order is
row-major, and you can either use -a 1.
You may replace the binary volume file by a JSON-formatted shape file. Please refer to Section V for details.
The time gate parameter is specified by three numbers: start time, end time and time step size (in seconds). In the above example, the configuration specifies a total time window of [0 1] ns, with a 0.1 ns resolution. That means the total number of time gates is 10.
MCX provides an advanced option, -g, to run simulations when the GPU memory is
limited. It specifies how many time gates to simulate concurrently. Users may
want to limit that number to less than the total number specified in the input
file - and by default it runs one gate at a time in a single simulation. But if
there's enough memory based on the memory requirement in Section II, you can
simulate all 10 time gates (from the above example) concurrently by using
-g 10 in which case you have to make sure the video card has at least
60*60*60*10*5=10MB of free memory. If you do not include the -g, MCX will
assume you want to simulate just 1 time gate at a time.. If you specify a
time-gate number greater than the total number in the input file, (e.g,
-g 20) MCX will stop when the 10 time-gates are completed. If you use the
autopilot mode (-A), then the time-gates are automatically estimated for you.
Starting from version 0.7.9, MCX accepts a JSON-formatted input file in addition to the conventional tMCimg-like input format. JSON (JavaScript Object Notation) is a portable, human-readable and “fat-free” text format to represent complex and hierarchical data. Using the JSON format makes a input file self-explanatory, extensible and easy-to-interface with other applications (like MATLAB).
A sample JSON input file can be found under the examples/quicktest folder. The
same file, qtest.json, is also shown below:
{
"Help": {
"[en]": {
"Domain::VolumeFile": "file full path to the volume description file, can be a binary or JSON file",
"Domain::Dim": "dimension of the data array stored in the volume file",
"Domain::OriginType": "similar to --srcfrom0, 1 if the origin is [0 0 0], 0 if it is [1.0,1.0,1.0]",
"Domain::LengthUnit": "define the voxel length in mm, similar to --unitinmm",
"Domain::Media": "the first medium is always assigned to voxels with a value of 0 or outside of
the volume, the second row is for medium type 1, and so on. mua and mus must
be in 1/mm unit",
"Session::Photons": "if -n is not specified in the command line, this defines the total photon number",
"Session::ID": "if -s is not specified in the command line, this defines the output file name stub",
"Forward::T0": "the start time of the simulation, in seconds",
"Forward::T1": "the end time of the simulation, in seconds",
"Forward::Dt": "the width of each time window, in seconds",
"Optode::Source::Pos": "the grid position of the source, can be non-integers, in grid unit",
"Optode::Detector::Pos": "the grid position of a detector, can be non-integers, in grid unit",
"Optode::Source::Dir": "the unitary directional vector of the photon at launch",
"Optode::Source::Type": "source types, must be one of the following:
pencil,isotropic,cone,gaussian,planar,pattern,fourier,arcsine,disk,fourierx,fourierx2d,
zgaussian,line,slit,pencilarray,pattern3d",
"Optode::Source::Param1": "source parameters, 4 floating-point numbers",
"Optode::Source::Param2": "additional source parameters, 4 floating-point numbers"
}
},
"Domain": {
"VolumeFile": "semi60x60x60.bin",
"Dim": [60,60,60],
"OriginType": 1,
"LengthUnit": 1,
"Media": [
{"mua": 0.00, "mus": 0.0, "g": 1.00, "n": 1.0},
{"mua": 0.005,"mus": 1.0, "g": 0.01, "n": 1.0}
]
},
"Session": {
"Photons": 1000000,
"RNGSeed": 29012392,
"ID": "qtest"
},
"Forward": {
"T0": 0.0e+00,
"T1": 5.0e-09,
"Dt": 5.0e-09
},
"Optode": {
"Source": {
"Pos": [29.0, 29.0, 0.0],
"Dir": [0.0, 0.0, 1.0],
"Type": "pencil",
"Param1": [0.0, 0.0, 0.0, 0.0],
"Param2": [0.0, 0.0, 0.0, 0.0]
},
"Detector": [
{
"Pos": [29.0, 19.0, 0.0],
"R": 1.0
},
{
"Pos": [29.0, 39.0, 0.0],
"R": 1.0
},
{
"Pos": [19.0, 29.0, 0.0],
"R": 1.0
},
{
"Pos": [39.0, 29.0, 0.0],
"R": 1.0
}
]
}
}
A JSON input file requiers several root objects, namely Domain,
Session, Forward and Optode. Other root sections, like
Help, will be ignored. Each object is a data structure providing
information indicated by its name. Each object can contain various sub-fields.
The orders of the fields in the same level are flexible. For each field, you
can always find the equivalent fields in the *.inp input files. For example,
The VolumeFile field under the Domain object is the same as Line#6
in qtest.inp; the RNGSeed under Session is the same as Line#2; the
Optode.Source.Pos is the same as the triplet in Line#3; the
Forward.T0 is the same as the first number in Line#5, etc.
An MCX JSON input file must be a valid JSON text file. You can validate your input file by running a JSON validator, for example http://jsonlint.com/ You should always use "" to quote a “name” and separate parallel items by “,”.
MCX accepts an alternative form of JSON input, but using it is not recommended.
In the alternative format, you can use “rootobj_name.field_name”: value
to represent any parameter directly in the root level. For example
{
"Domain.VolumeFile": "semi60x60x60.json",
"Session.Photons": 10000000,
...
}
You can even mix the alternative format with the standard format. If any input parameter has values in both formats in a single input file, the standard-formatted value has higher priority.
To invoke the JSON-formatted input file in your simulations, you can use the
-f command line option with MCX, just like using an .inp file. For
example:
mcx -A 1 -n 20 -f onecube.json -s onecubejson
The input file must have a .json suffix in order for MCX to recognize. If
the input information is set in both command line, and input file, the command
line value has higher priority (this is the same for .inp input files). For
example, when using -n 20, the value set in Session/Photons
is overwritten to 20; when using -s onecubejson, the
Session/ID value is modified. If your JSON input file is invalid,
MCX will quit and point out where the format is incorrect.
Starting from v0.7.9, MCX can also use a shape description file in the place of
the volume file. Using a shape-description file can save you from making a
binary .bin volume. A shape file uses more descriptive syntax and can be easily
understood and shared with others.
Samples on how to use the shape files are included under the example/shapetest folder.
The sample shape file, shapes.json, is shown below:
{
"MCX_Shape_Command_Help":{
"Shapes::Common Rules": "Shapes is an array object. The Tag field sets the voxel value for each
region; if Tag is missing, use 0. Tag must be smaller than the maximum media number in the
input file.Most parameters are in floating-point (FP). If a parameter is a coordinate, it
assumes the origin is defined at the lowest corner of the first voxel, unless user overwrite
with an Origin object. The default origin of all shapes is initialized by user's --srcfrom0
setting: if srcfrom0=1, the lowest corner of the 1st voxel is [0,0,0]; otherwise, it is [1,1,1]",
"Shapes::Name": "Just for documentation purposes, not parsed in MCX",
"Shapes::Origin": "A floating-point (FP) triplet, set coordinate origin for the subsequent objects",
"Shapes::Grid": "Recreate the background grid with the given dimension (Size) and fill-value (Tag)",
"Shapes::Sphere": "A 3D sphere, centered at C0 with radius R, both have FP values",
"Shapes::Box": "A 3D box, with lower corner O and edge length Size, both have FP values",
"Shapes::SubGrid": "A sub-section of the grid, integer O- and Size-triplet, inclusive of both ends",
"Shapes::XLayers/YLayers/ZLayers": "Layered structures, defined by an array of integer triples:
[start,end,tag]. Ends are inclusive in MATLAB array indices. XLayers are perpendicular to x-axis, and so on",
"Shapes::XSlabs/YSlabs/ZSlabs": "Slab structures, consisted of a list of FP pairs [start,end]
both ends are inclusive in MATLAB array indices, all XSlabs are perpendicular to x-axis, and so on",
"Shapes::Cylinder": "A finite cylinder, defined by the two ends, C0 and C1, along the axis and a radius R",
"Shapes::UpperSpace": "A semi-space defined by inequality A*x+B*y+C*z>D, Coef is required, but not Equ"
},
"Shapes": [
{"Name": "Test"},
{"Origin": [0,0,0]},
{"Grid": {"Tag":1, "Size":[40,60,50]}},
{"Sphere": {"Tag":2, "O":[30,30,30],"R":20}},
{"Box": {"Tag":0, "O":[10,10,10],"Size":[10,10,10]}},
{"Subgrid": {"Tag":1, "O":[13,13,13],"Size":[5,5,5]}},
{"UpperSpace":{"Tag":3,"Coef":[1,-1,0,0],"Equ":"A*x+B*y+C*z>D"}},
{"XSlabs": {"Tag":4, "Bound":[[5,15],[35,40]]}},
{"Cylinder": {"Tag":2, "C0": [0.0,0.0,0.0], "C1": [15.0,8.0,10.0], "R": 4.0}},
{"ZLayers": [[1,10,1],[11,30,2],[31,50,3]]}
]
}
A shape file must contain a Shapes object in the root level. Other
root-level fields are ignored. The Shapes object is a JSON array, with
each element representing a 3D object or setting. The object-class commands
include Grid, Sphere, Box etc. Each of these object include a
number of sub-fields to specify the parameters of the object. For example, the
Sphere object has 3 subfields, O, R and Tag. Field
O has a value of 1x3 array, representing the center of the sphere;
R is a scalar for the radius; Tag is the voxel values. The most
useful command is [XYZ]Layers. It contains a series of integer
triplets, specifying the starting index, ending index and voxel value of a
layered structure. If multiple objects are included, the subsequent objects
always overwrite the overlapping regions covered by the previous objects.
There are a few ways for you to use shape description records in your MCX
simulations. You can save it to a JSON shape file, and put the file name in
Line#6 of your .inp file, or set as the value for Domain.VolumeFile field in a
.json input file. In these cases, a shape file must have a suffix of .json.
You can also merge the Shapes section with a .json input file by simply
appending the Shapes section to the root-level object. You can find an example,
jsonshape_allinone.json, under examples/shapetest. In this case, you no longer
need to define the VolumeFile field in the input.
Another way to use Shapes is to specify it using the -P (or --shapes) command
line flag. For example:
mcx -f input.json -P '{"Shapes":[{"ZLayers":[[1,10,1],[11,30,2],[31,60,3]]}]}'
This will first initialize a volume based on the settings in the input .json
file, and then rasterize new objects to the domain and overwrite regions that
are overlapping.
For both JSON-formatted input and shape files, you can use the JSONlab toolbox [4] to load and process in MATLAB.
MCX may produces several output files depending user's simulation settings. Overall, MCX produces two types of outputs, 1) data accummulated within the 3D volume of the domain (volumetric output), and 2) data stored for each detected photon (detected photon data).
By default, MCX stores a 4D array denoting the fluence-rate at each voxel in
the volume, with a dimension of NxNyNz*Ng, where Nx/Ny/Nz are the voxel dimension
of the domain, and Ng is the total number of time gates. The output data are
stored in the format of single-precision floating point numbers. One may choose
to output different physical quantities by setting the -O option. When the
flag -X/--saveref is used, the output volume may contain the total diffuse
reflectance only along the background-voxels adjacent to non-zero voxels.
A negative sign is added for the diffuse reflectance raw output to distinguish
it from the fuence data in the interior voxels.
When photon-sharing (simultaneous simulations of multiple patterns) or photon-replay (the Jacobian of all source/detector pairs) is used, the output array may be extended to a 5D array, with the left-most/fastest index being the number of patterns Ns (in the case of photon-sharing) or src/det pairs (in replay), denoted as Ns.
Several data formats can be used to store the 3D/4D/5D volumetric output.
Starting in MCX v2023, .mc2 files are no longer the default output format for
MCX binary. Instead, JSON based JNIfTI (.jnii) files are used.
The .mc2 format is simply a binary dump of the entire volumetric data output,
consisted of the voxel values (single-precision floating-point) of all voxels and
time gates. The file contains a continuous buffer of a single-precision (4-byte)
5D array of dimension Ns*Nx*Ny*Nz*Ng, with the fastest index being the left-most
dimension (i.e. column-major, similar to MATLAB/FORTRAN).
To load the mc2 file, one should call loadmc2.m and must provide explicitly
the dimensions of the data. This is because mc2 file does not contain the data
dimension information.
Saving to .mc2 volumetric file is depreciated as we are transitioning towards JNIfTI/JData formatted outputs (.jnii).
The NIfTI-1 (.nii) format is widely used in neuroimaging and MRI community to store and exchange ND numerical arrays. It contains a 352 byte header, followed by the raw binary stream of the output data. In the header, the data dimension information as well as other metadata is stored.
A .nii output file can be generated by using -F nii in the command line.
The .nii file is widely supported among data processing platforms, including MATLAB and Python. For example
- niftiread.m/niftiwrite in MATLAB Image Processing Toolbox
- JNIfTI toolbox by Qianqian Fang (https://github.com/NeuroJSON/jnifti/tree/master/lib/matlab)
- PyNIfTI for Python http://niftilib.sourceforge.net/pynifti/intro.html
Starting in MCX v2023, JSON based JNIfTI (.jnii) files are used as the default
volumetric data output format.
The JNIfTI format represents the next-generation scientific data storage and exchange standard and is part of the US NIH-funded NeuroJSON initiative (https://neurojson.org) led by the MCX author Dr. Qianqian Fang. The NeuroJSON project aims at developing easy-to-parse, human-readable and easy-to-reuse data storage formats based on the ubiquitously supported JSON/binary JSON formats and portable JData data annotation keywords. In short, .jnii file is simply a JSON file with capability of storing binary strongly-typed data with internal compression and built in metadata.
The format standard (Draft 1) of the JNIfTI file can be found at
https://github.com/NeuroJSON/jnifti
A .jnii output file can be generated by using -F jnii in the command line.
The .jnii file can be potentially read in nearly all programming languages because it is 100% comaptible to the JSON format. However, to properly decode the ND array with built-in compression, one should call JData compatible libraries, which can be found at https://neurojson.org/#software
Specifically, to parse/save .jnii files in MATLAB, you should use
- JSONLab for MATLAB (https://neurojson.org/download/jsonlab) or install
octave-jsonlabon Fedora/Debian/Ubuntu jsonencode/jsondecodein MATLAB +jdataencode/jdatadecodefrom JSONLab (https://neurojson.org/download/jsonlab)
To parse/save .jnii files in Python, you should use
- PyJData module (https://neurojson.org/download/pyjdata) or install
python3-jdataon Debian/Ubuntu
In Python, the volumetric data is loaded as a dict object where data['NIFTIData']
is a NumPy ndarray object storing the volumetric data.
The binary JNIfTI file is also part of the JNIfTI specification and the NeuroJSON project. In comparison to text-based JSON format, .bnii files can be much smaller and faster to parse. The .bnii format is also defined in the BJData specification
https://github.com/NeuroJSON/bjdata
and is the binary interface to .jnii. A .bnii output file can be generated by
using -F bnii in the command line.
The .bnii file can be potentially read in nearly all programming languages because it was based on UBJSON (Universal Binary JSON). However, to properly decode the ND array with built-in compression, one should call JData compatible libraries, which can be found at https://neurojson.org/#software
Specifically, to parse/save .jnii files in MATLAB, you should use one of
- JSONLab for MATLAB (https://neurojson.org/download/jsonlab) or install
octave-jsonlabon Fedora/Debian/Ubuntu jsonencode/jsondecodein MATLAB +jdataencode/jdatadecodefrom JSONLab (https://neurojson.org/download/jsonlab)
To parse/save .jnii files in Python, you should use
- PyJData module (https://neurojson.org/download/pyjdata) or install
python3-jdataon Debian/Ubuntu
In Python, the volumetric data is loaded as a dict object where data['NIFTIData']
is a NumPy ndarray object storing the volumetric data.
If one defines detectors, MCX is able to store a variety of photon data when a photon
is captured by these detectors. One can selectively store various supported data fields,
including partial pathlengths, exit position and direction, by using the -w/--savedetflag
flag. The storage of detected photon information is enabled by default, and can be
disabled using the -d flag.
The detected photon data are stored in a separate file from the volumetric output. The supported data file formats are explained below.
The .mch file, or MC history file, is stored by default, but we strongly encourage users to adpot the newly implemented JSON/.jdat format for easy data sharing.
The .mch file contains a 256 byte binary header, followed by a 2-D numerical array
of dimensions #savedphoton * #colcount as recorded in the header.
typedef struct MCXHistoryHeader{
char magic[4]; // magic bits= 'M','C','X','H'
unsigned int version; // version of the mch file format
unsigned int maxmedia; // number of media in the simulation
unsigned int detnum; // number of detectors in the simulation
unsigned int colcount; // how many output files per detected photon
unsigned int totalphoton; // how many total photon simulated
unsigned int detected; // how many photons are detected (not necessarily all saved)
unsigned int savedphoton; // how many detected photons are saved in this file
float unitinmm; // what is the voxel size of the simulation
unsigned int seedbyte; // how many bytes per RNG seed
float normalizer; // what is the normalization factor
int respin; // if positive, repeat count so total photon=totalphoton*respin; if negative, total number is processed in respin subset
unsigned int srcnum; // number of sources for simultaneous pattern sources
unsigned int savedetflag; // number of sources for simultaneous pattern sources
unsigned int totalsource; // total source number when multiple sources are defined
int reserved[1]; // reserved fields for future extension
} History;
When the -q flag is set to 1, the detected photon initial seeds are also stored
following the detected photon data, consisting of a 2-D byte array of #savedphoton * #seedbyte.
To load the mch file, one should call loadmch.m in MATLAB/Octave.
Saving to .mch history file is depreciated as we are transitioning towards
JSON/JData formatted outputs (.jdat).
When -F jnii is specified, instead of saving the detected photon into the legacy .mch format,
a .jdat file is written, which is a pure JSON file. This file contains a hierachical data
record of the following JSON structure
{
"MCXData":{
"Info":{
"Version":
"MediaNum":
"DetNum":
...
"Media":{
...
}
},
"PhotonData":{
"detid":
"nscat":
"ppath":
"mom":
"p":
"v":
"w0":
},
"Trajectory":{
"photonid":
"p":
"w0":
},
"Seed":[
...
]
}
}
where "Info" is required, and other subfields are optional depends on users' input. Each subfield in this file may contain JData 1-D or 2-D array constructs to allow storing binary and compressed data.
Although .jdat and .jnii have different suffix, they are both JSON/JData files and can be opened/written by the same JData compatible libraries mentioned above, i.e.
For MATLAB
- JSONLab for MATLAB (https://neurojson.org/download/jsonlab) or install
octave-jsonlabon Fedora/Debian/Ubuntu jsonencode/jsondecodein MATLAB +jdataencode/jdatadecodefrom JSONLab (https://neurojson.org/download/jsonlab)
For Python
- PyJData module (https://neurojson.org/download/pyjdata) or install
python3-jdataon Debian/Ubuntu
In Python, the volumetric data is loaded as a dict object where data['MCXData']['PhotonData']
stores the photon data, data['MCXData']['Trajectory'] stores the trajectory data etc.
For debugging and plotting purposes, MCX can output photon trajectories, as polylines,
when -D M flag is attached, or mcxlab is asked for the 5th output. Such information
can be stored in one of the following formats.
By default, MCX stores the photon trajectory data in to a .mct file MC trajectory, which
uses the same binary format as .mch but renamed as .mct. This file can be loaded to
MATLAB using the same loadmch.m function.
Using .mct file is depreciated and users are encouraged to migrate to .jdat file as described below.
When -F jnii is used, MCX merges the trajectory data with the detected photon and
seed data and saved as a JSON-compatible .jdat file. The overall structure of the
.jdat file as well as the relevant parsers can be found in the above section.
MCXLAB is the native MEX version of MCX for MATLAB and GNU Octave. It includes the entire MCX code in 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 simplified without involving disk files.
Please read the mcxlab/README.txt file for more details on how to install and
use MCXLAB.
Please also browse this interactive Jupyter Notebook based MCXLAB tutorial to see a suite of examples showing the key functionalities of MCXLAB (using GNU Octave).
PMCX is the native binary binding of MCX for Python 3.6 or newer. Similar to MCXLAB, PMCX can run GPU-based simulations inside Python environment with efficient in-memory inputs and outputs.
Please read the pmcx/README.txt file for more details on how to install and
use PMCX.
Please also browse this interactive Jupyter Notebook based PMCX tutorial to see a suite of examples showing the key functionalities of PMCX.
MCX Studio is a graphics user interface (GUI) for MCX. It gives users a straightforward way to set the command line options and simulation parameters. It also allows users to create different simulation tasks and organize them into a project and save for later use. MCX Studio can be run on many platforms such as Windows, GNU Linux and Mac OS.
To use MCX Studio, it is suggested to put the mcxstudio binary in the same directory as the mcx command; alternatively, you can also add the path to mcx command to your PATH environment variable.
Once launched, MCX Studio will automatically check if mcx binary is in the search path, if so, the “GPU” button in the toolbar will be enabled. It is suggested to click on this button once, and see if you can see a list of GPUs and their parameters printed in the output field at the bottom part of the window. If you are able to see this information, your system is ready to run MCX simulations. If you get error messages or not able to see any usable GPU, please check the following:
- are you running MCX Studio/MCX on a computer with a supported card?
- have you installed the CUDA/NVIDIA drivers correctly?
- did you put mcx in the same folder as mcxstudio or add its path to PATH?
If your system has been properly configured, you can now add new simulations by clicking the “New” button. MCX Studio will ask you to give a session ID string for this new simulation. Then you are allowed to adjust the parameters based on your needs. Once you finish the adjustment, you should click the “Verify” button to see if there are missing settings. If everything looks fine, the “Run” button will be activated. Click on it once will start your simulation. If you want to abort the current simulation, you can click the “Stop” button.
You can create multiple tasks with MCX Studio by hitting the “New” button again. The information for all session configurations can be saved as a project file (with .mcxp extension) by clicking the “Save” button. You can load a previously saved project file back to MCX Studio by clicking the “Load” button.
MCX output consists of two parts, the flux volume file and messages printed on the screen.
An mc2 file contains the fluence-rate distribution from the simulation in the given medium. By default, this fluence-rate is a normalized solution (as opposed to the raw probability) therefore, one can compare this directly to the analytical solutions (i.e. Green's function). The order of storage in the mc2 files is the same as the input file: i.e., if the input is row-major, the output is row-major, and so on. The dimensions of the file are Nx, Ny, Nz, and Ng where Ng is the total number of time gates.
By default, MCX produces the Green's function of the fluence rate for the given domain and source. Sometime it is also known as the time-domain “two-point” function. If you run MCX with the following command
mcx -f input.inp -s output ....
the fluence-rate data will be saved in a file named “output.dat” under the
current folder. If you run MCX without -s output, the output file will be
named as input.inp.dat.
To understand this further, you need to know that a fluence-rate (Phi(r,t)) is measured by number of particles passing through an infinitesimal spherical surface per unit time at a given location regardless of directions. The unit of the MCX output is “W/mm2 = J/(mm2s)”, if it is interpreted as the “energy fluence-rate” [6], or “1/(mm2s)”, if the output is interpreted as the “particle fluence-rate” [6].
The Green's function of the fluence-rate means that it is produced by a unitary source. In simple terms, this represents the fraction of particles/energy that arrives a location per second under the radiation of 1 unit (packet or J) of particle or energy at time t=0. The Green's function is calculated by a process referred to as the “normalization” in the MCX code and is detailed in the MCX paper [6] (MCX and MMC outputs share the same meanings).
Please be aware that the output flux is calculated at each time-window defined in the input file. For example, if you type
0.e+00 5.e-09 1e-10 # time-gates(s): start, end, step
in the 5th row in the input file, MCX will produce 50 fluence-rate snapshots,
corresponding to the time-windows at [0 0.1] ns, [0.1 0.2]ns ... and
[4.9,5.0] ns. To convert the fluence rate to the fluence for each
time-window, you just need to multiply each solution by the width of the
window, 0.1 ns in this case. To convert the time-dependent fluence-rate to
continuous-wave (CW) fluence (fluence in short), you need to integrate the
fluence-rate along the time dimension. Assuming the fluence-rate after 5 ns is
negligible, then the CW fluence is simply sum(flux_i*0.1 ns, i=1,50). You can
read mcx/examples/validation/plotsimudata.m and
mcx/examples/sphbox/plotresults.m for examples to compare an MCX output with
the analytical fluence-rate/fluence solutions.
One can load an .mc2 output file into Matlab or Octave using the loadmc2
function in the {mcx root}/utils folder.
To get a continuous-wave solution, run a simulation with a sufficiently long time window, and sum the flux along the time dimension, for example
mcx=loadmc2('output.mc2',[60 60 60 10],'float');
cw_mcx=sum(mcx,4);
Note that for time-resolved simulations, the corresponding solution in the
results approximates the flux at the center point of each time window. For
example, if the simulation time window setting is
[t0,t0+dt,t0+2dt,t0+3dt...,t1], the time points for the snapshots stored in
the solution file is located at [t0+dt/2, t0+3*dt/2, t0+5*dt/2, ... ,t1-dt/2]
A more detailed interpretation of the output data can be found at http://mcx.sf.net/cgi-bin/index.cgi?MMC/Doc/FAQ#How_do_I_interpret_MMC_s_output_data
MCX can also output “current density” (J(r,t), unit W/m^2, same as Phi(r,t)) - referring to the expected number of photons or Joule of energy flowing through a unit area pointing towards a particular direction per unit time. The current density can be calculated at the boundary of the domain by two means:
- using the detected photon partial path output (i.e. the second output of mcxlab.m), one can compute the total energy E received by a detector, then one can divide E by the area/aperture of the detector to obtain the J(r) at a detector (E should be calculated as a function of t by using the time-of-fly of detected photons, the E(t)/A gives J(r,t); if you integrate all time gates, the total E/A gives the current I(r), instead of the current density).
- use
-X 1or--saveref/cfg.issaverefoption in mcx to enable the diffuse reflectance recordings on the boundary. the diffuse reflectance is represented by the current density J(r) flowing outward from the domain.
The current density has, as mentioned, the same unit as fluence rate, but the
difference is that J(r,t) is a vector, and Phi(r,t) is a scalar. Both measuring
the energy flow across a small area (the are has direction in the case of J)
per unit time.
You can find more rigorous definitions of these quantities in Lihong Wang's Biomedical Optics book, Chapter 5.
Timing information is printed on the screen (stdout). The clock starts (at time T0) right before the initialization data is copied from CPU to GPU. For each simulation, the elapsed time from T0 is printed (in ms). Also the accumulated elapsed time is printed for all memory transaction from GPU to CPU.
When a user specifies -D P in the command line, or set
cfg.debuglevel='P', MCX or MCXLAB prints a progress bar showing the percentage
of completition.
To maximize MCX's performance on your hardware, you should follow the best practices guide listed below:
MCX is highly scalable, providing linear-speedup as long as you provide the GPU cores it can use. As a result, the better the GPU you use, the higher the speed you can get. An enthusiastic-grade GPU, such as RTX 4070Ti (~$700), can be 12x faster than an low-end laptop RTX 4050 GPU even within the same generation.
MCX can readily take advantage of multiple GPUs if you have it installed. The MCX simulation speed scales nearly linearly as the number of GPUs increases. So, to maximize MCX performance, get at least a middle-level or high-end consumer grade GPU; if you need more speed, throw in more GPUs will cut down the runtime.
It has been shown that MCX's speed is related to the thread number (-t).
Generally, the more threads, the better speed, until all GPU resources are
fully occupied. For higher-end GPUs, a thread number over 10,000 is
recommended. Please use the autopilot mode, -A, to let MCX determine the
“optimal” thread number when you are not sure what to use.
MCX contains modified versions of the below source codes from other open-source projects (with a compatible license).
- Files: src/cJSON folder
- Copyright (c) 2009 Dave Gamble
- URL: https://github.com/DaveGamble/cJSON
- License: MIT License, https://github.com/DaveGamble/cJSON/blob/master/LICENSE
- Files: mcxstudio/glscene/*
- Copyright (c) GLScene developers
- URL: http://glscene.org, https://sourceforge.net/p/glscene/code/HEAD/tree/branches/GLSceneLCL/
- License: Mozilla Public License 2.0 (MPL-2), https://sourceforge.net/p/glscene/code/HEAD/tree/trunk/LICENSE
- Comment: A subset of the GLSceneLCL branch is included as part of the MCX source code tree to allow compilation of the MCX Studio binary on various platforms without needing to install the full package.
- Files: mcx/src/mcxstudio/mcxview.pas
- Copyright (c) 2003 Jürgen Abel
- License: Mozilla Public License 2.0 (MPL-2), https://sourceforge.net/p/glscene/code/HEAD/tree/trunk/LICENSE
- Comment: The MCX volume renderer (mcxviewer) was adapted based on the Texture3D Example provided by the GLScene Project (http://glscene.org). The original author of this example is Jürgen Abel.
- Files: mcxstudio/synapse/*
- Copyright (c) 1999-2017, Lukas Gebauer
- URL: http://www.ararat.cz/synapse/
- License: MIT License or LGPL version 2 or later or GPL version 2 or later
- Comment: A subset of the Synapse units is included as part of the MCX source code tree to allow compilation of the MCX Studio binary on various platforms without needing to install the full package.
- Files: src/zmat/*
- Copyright: 2019-2023 Qianqian Fang
- URL: https://github.com/fangq/zmat
- License: GPL version 3 or later, https://github.com/fangq/zmat/blob/master/LICENSE.txt
- Files: src/zmat/lz4/*
- Copyright: 2011-2020, Yann Collet
- URL: https://github.com/lz4/lz4
- License: BSD-2-clause, https://github.com/lz4/lz4/blob/dev/lib/LICENSE
- Files: src/zmat/easylzma/*
- Copyright: 2009, Lloyd Hilaiel, 2008, Igor Pavlov
- License: public-domain
- Comment: All the cruft you find here is public domain. You don't have to credit anyone to use this code, but my personal request is that you mention Igor Pavlov for his hard, high quality work.
- Files: utils/{islicer.m, slice3i.m, image3i.m}
- Copyright (c) 2009 Anders Brun, [email protected]
- URL: https://www.mathworks.com/matlabcentral/fileexchange/25923-myslicer-make-mouse-interactive-slices-of-a-3-d-volume
- License: BSD-3-clause License, https://www.mathworks.com/matlabcentral/fileexchange/25923-myslicer-make-mouse-interactive-slices-of-a-3-d-volume#license_modal
- Files: filter/*
- Copyright (c) 2018 Yaoshen Yuan, 2018 Qianqian Fang
- URL: https://github.com/fangq/GPU-ANLM/
- License: MIT License, https://github.com/fangq/GPU-ANLM/blob/master/LICENSE.txt
- Files: pymcx/*
- Copyright (c) 2020 Maxime Baillot <maxime.baillot.1 at ulaval.ca>
- URL: https://github.com/fangq/GPU-ANLM/
- License: GPL version 3 or later, https://github.com/4D42/pymcx/blob/master/LICENSE.txt
- Files: src/pybind11/*
- Copyright (c) 2016 Wenzel Jakob [email protected]
- URL: https://github.com/pybind/pybind11/
- License: BSD-style license, https://github.com/pybind/pybind11/blob/master/LICENSE
-
[Fang2009] Qianqian Fang and David A. Boas, "Monte Carlo Simulation of Photon Migration in 3D Turbid Media Accelerated by Graphics Processing Units," Optics Express, vol. 17, issue 22, pp. 20178-20190 (2009).
-
[Yu2018] Leiming Yu, Fanny Nina-Paravecino, David Kaeli, Qianqian Fang, “Scalable and massively parallel Monte Carlo photon transport simulations for heterogeneous computing platforms,” J. Biomed. Opt. 23(1), 010504 (2018).
-
[Yan2020] Shijie Yan and Qianqian Fang* (2020), "Hybrid mesh and voxel based Monte Carlo algorithm for accurate and efficient photon transport modeling in complex bio-tissues," Biomed. Opt. Express, 11(11) pp. 6262-6270. https://www.osapublishing.org/boe/abstract.cfm?uri=boe-11-11-6262
If you use MCX in your research, the author of this software would like you to cite the above papers in your related publications.
Links:
- [1] http://developer.nvidia.com/cuda-downloads
- [2] http://www.nvidia.com/object/cuda_gpus.html
- [3] http://en.wikipedia.org/wiki/Row-major_order
- [4] https://neurojson.org/jsonlab
- [5] http://science.jrank.org/pages/60024/particle-fluence.html
- [6] http://www.opticsinfobase.org/oe/abstract.cfm?uri=oe-17-22-20178