- Author: Qianqian Fang (q.fang at neu.edu)
- License: GNU General Public License version 3 (GPLv3)
- Version: 2.0 (v2023, Heroic Hadron)
- Website: http://mcx.space
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 v2023 is a milestone release since v2020 released 3 years ago. It contains all the new features introduced in the two previous unofficial releases, v2022.10 and v2021.2, along with extensive continuous integration (CI) development and numerous bug fixes.
Specifically, MCX v2023 officially ships a major new feature - split-voxel MC (SVMC), published in Biomedical Optics Express by Shijie Yan and Qianqian Fang, see Yan2020 for details. Shortly, SVMC provides a level of accuracy close to mesh-based MC (MMC) in modeling curved boundaries but it is 4x to 6x faster than MMC. Several demo scripts of SVMC can be found in the MCXLAB package under examples/demo_svmc_*. In addition, MCX v2023 supports GPU-based polarized light simulation, see our JBO paper Yan2022. Moreover, an "RF replay" algorithm was implemented by Pauliina Hirvi et al. to create frequency-domain (RF) Jacobians for both amplitude and phase components. Please read the details in Hirvi2023. This release also includes both the web-client and server scripts for MCX Cloud - an in-browser MC simulator as we reported in Fang2022. Lastly, MCX v2023 provides an official Python mcx module (pmcx) to run stream-lined MCX simulations in Python, offering mcxlab-like interface.
Starting in MCX v2023, we have completed the migration from MCX-specific binary output formats (.mc2/.mch) to human-readable, extensible and future-proof JSON-based portable data formats defined by the NeuroJSON project. The NeuroJSON project aims at simplify scientific data exchange using portable data formats that are readable, searchable, shareable, can be readily validated and served in the web and cloud. The NeuroJSON project is also led by MCX's author, Dr. Qianqian Fang, funded by the US NIH U24-NS124027 grant.
As a result of this migration, the MCX executable's default output formats are now
.jnii for volumetric output data, and .jdat for detected photon/trajectory data.
Both data formats are JSON compatible. Details on how to read/write these data files
can be found below.
In summary, v2023 is packed with exciting updates, including
- Introduced Split voxel MC (SVMC) to accurately model curved boundaries
- GPU polarized light modeling (Stokes) with 900x speedup
- RF replay to build frequency-domain Jacobians for amplitude and phase
- Web-based MCX Cloud platform including web-client and server scripts
- pymcx - an mcxlab-like Python module for running MCX simulations in Python
- Added Debian/Ubuntu packages for easy installation
- Added a unified command line interface, photon, to call mcx/mcxcl/mmc
- Fine-tuned Windows installer
- Extensively developed Github Action for automated building and packaging of mcx
- Adopted standardized NeuroJSON JNIfTI and JData formats to ease data exchange
- New source types: hyperboloid and ring (annulus,annulus sector)
A detailed list of updates is summarized below (key features marked with “*”):
Updates since v2022.10:
- 2023-09-22 [9185b5d] fix 64bit macos gui crash, #184
- 2023-09-21 [393c620] fix valgrind warnings
- 2023-09-20 [1adb6c6] * disable OpenGL functionalities when building 64bit mac, fix #184
- 2023-09-19 [9a5dd5b] update octave package files
- 2023-09-17 [9e9e699] update mcx command cheatsheet
- 2023-09-17 [ad5a1dd] update all documentation, bump pmcx to v0.1.3
- 2023-09-17 [5b8a06f] add comments to nightly build script for deployment
- 2023-09-16 [d2a2ae3] update deploy script after reformat
- 2023-09-15 [387df65] link libomp.a on mac
- 2023-09-15 [bf6843f] simplify linkopt
- 2023-09-15 [4ee145e] update help info
- 2023-09-13 [ffc8ab0] * support ASCII escape code in Windows terminals
- 2023-09-12 [24bb9e1] add path to lazbuild
- 2023-09-12 [6820c04] build mcxstudio on mac
- 2023-09-12 [9f3c3c2] print verbose info on mac
- 2023-09-12 [48d4f2f] test macos-12 runner
- 2023-09-11 [57519b9] force -std=c++11 to build oct on older gcc
- 2023-09-07 [24c3533] remove redundant functions in mcxlab
- 2023-09-05 [53e2681] update pmcx after fixing the regression due to #164
- 2023-09-04 [0b98843] * fix regression caused by #164 for mus=0 region patch in #164 breaks https://github.com/fangq/mmc/blob/master/mmclab/example/demo_dualmesh_output.m
- 2023-09-04 [287671b] Merge pull request #182 from fangq/ringsrc
- 2023-09-03 [ad6ff4c] * compact implementation of ring source, close #181
- 2023-09-01 [b0bad9f] renormalize dir vector after each rotation, suggested by @ShijieYan
- 2023-09-01 [4fe8e43] highlight link and version with ascii color
- 2023-08-28 [1a2f291] fix macos nightly build
- 2023-08-27 [2a5f27c] fix ci build
- 2023-08-27 [b9f22ad] print code name, print min CUDA arch support
- 2023-08-25 [f23188b] Update pmcx jupyter notebook
- 2023-08-25 [0be475b] bump pmcx version to 0.1.1 to fix critical bug #180
- 2023-08-25 [eaf31de] * [bug] critical! pmcx assumes incorrect default focal length, fix #180
- 2023-08-25 [ddbbaf3] * [bug]: fix default outputformat when parsing json input, fix #179
- 2023-08-24 [acfea7d] allow to link with libomp on macos with clang
- 2023-08-21 [88eba94] fix macos mcx package path
- 2023-08-21 [1c9efee] adjust cmake build path
- 2023-08-21 [a2e73b9] add openmp to matlab mex
- 2023-08-21 [f1d3829] add NO_IMPLICIT_LINK_TO_MATLAB_LIBRARIES in cmake
- 2023-08-20 [4db668a] fix python windows build error
- 2023-08-20 [ec119b6] bump pmcx version number, rebuild python module
- 2023-08-19 [a0a2a9b] * add pmcx utility functions and its test by Ivy Yen
- 2023-08-15 [73dae89] update pip version
- 2023-08-15 [d2ff843] use pip3 in check-pypi-upload.sh
- 2023-08-15 [4f9e278] use UPLOAD_TO_PYPI flag as deploy condition
- 2023-08-15 [c6d4258] limit travis to only build on master
- 2023-08-15 [4775c4f] rearrange folder in travis
- 2023-08-15 [18255c7] install missing twine, move python files to top level
- 2023-08-15 [9255345] try uploading python macos module from travis
- 2023-08-12 [af3d386] fix remaining mc2 format flag
- 2023-08-12 [3187ece] * switch from custom mc2/mch formats to jnii and jdat as default
- 2023-08-12 [780c7ab] support mcxlab('version'), let pmcx to read 1D detpos,prop,polprop
- 2023-08-12 [aa9b2f4] update documentation, prepare for v2023 release
- 2023-08-07 [ac893cd] * mcxplotvol: allow keeping x/y/z slice when switching between 4th dimension
- 2023-08-07 [9aaba97] fix photon sharing 0 output issue in negative patterns
- 2023-08-05 [da0beda] padding -0 instead of 0 when saving dref with mua_float medium
- 2023-08-04 [82367f0] simplify dref/flux separation
- 2023-08-04 [97fff3e] update zmatlib, use miniz, drop zlib for easy deployment
- 2023-08-03 [4fbb4d6] bump pmcx version to 0.0.14
- 2023-08-03 [aaedf35] handle mirror bc in the reflection code
- 2023-08-03 [198cd34] * initial support of negative source and negative-patterns some test still fails, but feature is mostly working, need more work
- 2023-07-27 [081c382] parse outputtype in json2mcx
- 2023-07-21 [87167cb] simplify mua->0 approximation, drop high order term, #164
- 2023-07-21 [f063fd6] disable macos runner, macos no longer supports CUDA see actions/runner-images#7838
- 2023-07-21 [3067a26] fix incorrect handling of near-zero mus, fix #174, fix test
- 2023-06-30 [ac06b05] CI: compress with upx on Linux
- 2023-06-29 [527a5cc] add header, format with black, update action runners, #172
- 2023-05-21 [540931d] * support trajectory-only mode with debuglevel=T
- 2023-05-20 [0100212] * mcxplotphotons: plotting photon tracks with patch and show weights
- 2023-05-20 [615af1b] avoid recursion and segfault when resetting device
- 2023-05-16 [c327541] add demo to build RF Jacobians using replay, by Pauliina Hirvi
- 2023-05-05 [bd44f65] reformat pmcx python units with black
- 2023-05-05 [c12310b] added cwdref function to compute CW diffuse reflectance
- 2023-05-05 [0bd643e] added detweight function (using only numpy) to the utils.py in pmcx
- 2023-05-04 [31c0fa3] fix incorrect stat.unitinmm output
- 2023-04-29 [0c6b358] use svg vector graphs in mcxlab tutorial
- 2023-04-29 [bed8f08] Update plots with GPU runtime outputs
- 2023-04-29 [688ac78] Support mcxlabcl for non-GPU runtime on colab, add srctype tutorial
- 2023-04-28 [4f53e12] add examples on getting trajectory data
- 2023-04-27 [f271501] Update mcxlab jupyter notebook based tutorial
- 2023-04-27 [be5a420] * add jupyter-notebook based mcxlab tutorial
- 2023-04-26 [eb68720] make static linking default on Windows
- 2023-04-25 [e315dfe] fix incorrect comment regarding gaussian src, fix #165
- 2023-04-16 [b78c4e3] fix inaccurate output unit for energy output time
- 2023-04-15 [2fd3594] accept jobs submitted from https://mcx.space/cloud
- 2023-04-15 [7515611] * fix mcxcloud job max duration bug, kill runtime>1min
- 2023-04-13 [70b3b5e] Add photon replay demo codes for pmcx in jupyter notebook
- 2023-04-13 [855aa40] pmcx: support photon replay, accept detphotons input
- 2023-04-13 [8a49fe3] switch from cmake back to Makefile
- 2023-04-01 [c2591aa] ask cmake to create Makefile
- 2023-03-22 [67d1128] add pmcx jupyter notebook tutorial
- 2023-03-22 [291adf5] allow mus=0, avoid unnecessary casting of scalars to double
- 2023-03-17 [3a4f7ed] fix fluence for mua -> 0
- 2023-03-15 [46b4311] remove explicit dependency to GLScene in mcxstudio
- 2023-03-10 [193158c] make volume rendering window available on main gui
- 2023-03-10 [2d09e54] allow Open project dialog to also load nii and jnii files for rendering
- 2023-03-09 [63a626d] fix progress bar stalling when setting cfg.issavedet to 3
- 2023-03-08 [8cab50f] add descriptions on how to start mcxstudio on an Mac, fix #162
- 2023-03-08 [da49503] allow early termination if -d 3 or cfg.issavedet=3 is set
- 2023-03-04 [5d4d3c6] transition from openjdata.org to neurojson.org, fix #161
- 2023-03-02 [3c29fa5] * mcxstudioL: loading and rendering jnifti based volume file
- 2023-03-01 [b99f9a9] mcxstudioL: loading portable JSON/JNIFTI based MCX output data files
- 2023-02-09 [32e3aef] fix RF replay in mcx binary, allow tweaking replay Jacobian for Born approx
- 2023-02-05 [9abd3f3] * adding additional native python pmcx functions
- 2023-02-05 [63b80f4] add missing pmcx file
- 2023-02-05 [e77ff2f] allow pmcx to use mixed binary extension and native function
- 2023-02-01 [d9b2f2b] * fix unmatched unit for RF replay, thanks to Pauliina Hirvi
- 2023-02-01 [ce2a65c] add the l/length option in help info
- 2023-02-01 [e67feed] support outputtype=length/l for saving total path lengths per voxel
- 2023-01-25 [4344574] fix windows compilation error
- 2023-01-25 [a7ce222] permit 3D plotting of DMMC output double-precision nii files
- 2023-01-22 [cdabf68] automatically replace RCS keywords in pmcx action
- 2023-01-22 [e527f4b] fix incomplete handling bc and isreflect setting combinations, fix #160
- 2023-01-20 [daa1dea] use standard CFLAGS and CPPFLAGS in compilation, remove --std99 error for g++
- 2023-01-11 [def38f6] Merge pull request #159 from matinraayai/master
- 2023-01-11 [e077f86] Bump pmcx version to 0.0.7.
- 2023-01-10 [7af2ea2] bump pmcx version to 0.0.6
- 2023-01-10 [820ce88] build macos binary wheels
- 2023-01-09 [4f969d1] Removed the macOS builder VM.
- 2023-01-09 [dd47cda] Updated README.md for pmcx.
- 2023-01-09 [82ef430] Final version.
- 2022-12-08 [eb9322f] Added Windows Wheel building job + fixed compilation errors for windows.
- 2022-11-18 [d5a9beb] Update build_linux_manywheel.yml
- 2022-11-18 [816c55f] Added Github workflow.
- 2022-10-15 [084ffc1] update cmake file and remove zmat from pymcx and mex
- 2022-10-14 [04000e2] remove zmatlib and ubj as dependency to mex and oct
- 2022-10-14 [d033520] fix negative respin number bug
- 2022-10-13 [ec4a29d] update version strings
- 2022-10-13 [a3fe4ce] remove warning on replay output
- 2022-10-10 [660dd31] update three.js to r145, fix volume render, fix thumbnail
Updates since v2021.2:
- 2022-10-08 [eaedca7] update installer to 2022.10
- 2022-10-08 [c31a0e2] update mcx version number to v2022.10
- 2022-10-05 [dc42951] prevent nan where log(rand) is calculated
- 2022-10-05 [63ffc1e] fix nan in detected photon data when using hyperboloid src, see https://groups.google.com/g/mcx-users/c/vyfHbzEO-0M/m/JzlpEZ3TBwAJ
- 2022-09-07 [e281f3e] * allow to preview continuously varying medium (4D cfg.vol)
- 2022-08-19 [10330ef] fix windows compilation error
- 2022-08-17 [bbb4425] prevent zero-valued mus creating nan, #133
- 2022-08-12 [51f42f5] fix mcxlab log printing caused by commit f3beb75a
- 2022-08-12 [7058785] * Lambertian launch for all focusable sources when focal-length is -inf
- 2022-07-28 [6d64c0b] fix incorrect flag for skipvoid
- 2022-06-27 [3d4fb26] partially fix rf replay
- 2022-06-04 [8af3631] fix line source
- 2022-05-22 [149b1ef] make code compile on windows
- 2022-05-20 [e87bb49] use consistent file naming convention, remove outdated units
- 2022-05-20 [45d84d3] * complete reformat source code using astyle, always run make pretty before committing
- 2022-05-20 [aff8ff0] add source code formatting option
- 2022-05-20 [f3beb75] use MATLAB_MEX_FILE to determine container environment
- 2022-05-18 [1295024] * fix incorrect trajectory id, fix #147
- 2022-05-18 [ccd2deb] fix macro condition
- 2022-05-18 [6f4ee88] use MCX_CONTAINER and env macros to replace extra PYMCX_CONTAINER
- 2022-05-16 [6fa1580] avoid using clear all and ~ in return value
- 2022-05-16 [21f9bd7] merge changes with @ShijieYan's svmc fix
- 2022-05-16 [8b2740f] debugging svmc crashes
- 2022-05-16 [7582a6e] fix svmc issue after patch f8da832f11b751c07d33c77dd7d428a2c75a888b
- 2022-05-15 [188ac2a] Added Pybind11's license info to README.md.
- 2022-05-15 [86529cf] Added PYMCX_CONTAINER compilation macro. Added support for extracting MCX_ERRORs like Mex + author fix.
- 2022-05-15 [b58ad88] Renamed gpu_info to gpuinfo for consistency.
- 2022-05-15 [0582974] changed issaveref to accept ints.
- 2022-05-15 [1cf1b3b] Added py::value_error handling + additional error checking for volume assurance.
- 2022-05-15 [7df8938] Added better + more informative exception handling for pymcx.
- 2022-05-15 [6a741e8] Changed reinterpret_casts to direct object construction + added the stat dict to the output dict + defined PYBIND11_DETAILED_ERROR_MESSAGES for easier debugging.
- 2022-05-15 [e4547ba] add pybind11 as submodule to build pymcx
- 2022-05-13 [f8da832] * fix cyclic bc demo and srctype demo error, svmc still not working
- 2022-05-13 [4bd3974] report register counts on sm_60 using nvcc --ptxas-options=-v
- 2022-05-13 [e8f6a2d] fix cfg.unitinmm does not exist error
- 2022-05-13 [447975f] complete dda ray-marching, cube60 benchmark gain 40% speed, others are similar
- 2022-05-12 [b873f90] add integer voxel indices to avoid nextafter
- 2022-05-11 [32d46cd] merge additional updates from mcxcl version of json2mcx, #139
- 2022-05-11 [3d6d7df] fix bugs in json2mcx, #139
- 2022-05-08 [3b1c320] Removed nlhs argument left from Matlab.
- 2022-05-08 [61cc994] Fixed issue with std::cout and std::cerr flush.
- 2022-05-06 [d9793e9] Added working setup.py.
- 2022-05-03 [a3e47c8] Minor fix.
- 2022-05-02 [c9bedd6] Moved validate_config.
- 2022-05-02 [5427ece] Working prototype with different volume configs.
- 2022-05-02 [739b7ea] Moved some stuff to interface-common.cpp.
- 2022-05-02 [d852a87] Minor bug fixes.
- 2022-05-01 [f4cd3c3] Added kwargs version of mcx.
- 2022-05-01 [baa5fdd] Skeleton is done.
- 2022-04-24 [e919716] Prints GPU info.
- 2022-04-23 [0c7b6a7] Working interface and CMakeLists.txt file.
- 2022-04-19 [c710c3c] update ubj parser, update jnifti metadata
- 2022-04-15 [4033c54] mcxlab bug fix: digimouse atlas voxel size is 0.2mm, not 0.8mm
- 2022-04-15 [9b17eee] critical bug fix: digimouse atlas voxel size is 0.2mm, not 0.8mm
- 2022-03-31 [df6c311] Add viewing axis selection
- 2022-03-25 [04f1565] Add optimized isosurface rendering; add the ability to view cross-sectional slices of geometry
- 2022-03-23 [200decb] Remove testing files
- 2022-03-23 [e434553] Remove unnecessary variable
- 2022-03-22 [ebd5bee] * update ubj to support BJData Draft 2, add JNIfTI DataInfo, fix #142
- 2022-03-21 [6de0855] Enable event-based repainting; re-add shader refinement, remove animation frame bugs; remove unnecessary shader branches and discards
- 2022-03-19 [7ef65a8] Added event-based repainting; shader optimizations
- 2022-03-05 [a93f6fa] save user info in local storage to avoid retyping
- 2022-03-05 [34d9afa] fix SaveDataFlag invisible bug
- 2022-02-18 [f051314] add missing voxel unit
- 2022-02-03 [23bf5b2] lowering default photon number so it can be launched on most gpus
- 2022-01-31 [220d9c2] fix incorrect type for gsmatrix
- 2022-01-31 [28e20d6] fix windows vs warning
- 2022-01-29 [6a9ad2f] update mcx_utils to use the Mie interface
- 2022-01-29 [13679e9] fix compilation issue of mcx_mie.cpp using MSVC, close #138
- 2022-01-28 [d7daf57] manually resolve complaint in CUDA 9
- 2022-01-28 [e99edb2] update .travis.yml
- 2022-01-28 [533c8ce] manually add mcx_mie in Makefile
- 2022-01-28 [e56b5cb] improve complex arithmetic compatablity with MSVC
- 2022-01-27 [a0ed0e7] add Mie function modules into cmake
- 2022-01-27 [c350c67] seperate Mie scattering functions from mcx_utils.h
- 2022-01-27 [0d51bb7] add missing i detflag in command line
- 2022-01-27 [9b74e4b] fix: add save detector flag for Stokes vectors
- 2022-01-26 [077060a] use static_cast in mcxlab so that cfg.vol can be realloc in mcx_shapes
- 2022-01-26 [8503125] do not reset cfg.vol when rasterizing cfg.shapes
- 2022-01-26 [3f22070] fix normalization in multiple detector RF replay
- 2022-01-26 [cdfd468] apply normalization to both real and imaginary jacobain for RF replay
- 2022-01-26 [87a310e] one can use ~ to ignore fluence output in octave, not in matlab
- 2022-01-26 [d45f084] allow users to explicitly disable fluence output by accepting cfg.issave2pt
- 2022-01-25 [376a730] partial fix to RF Jacobian calculation, need verification
- 2022-01-25 [d6e2b9e] NaN value in mua_float and muamus_float signifies a 0-value voxel
- 2022-01-24 [c9f2ad9] force threejs version to avoid breaking update in R136
- 2022-01-14 [51483eb] add template specialization for polarized mode
- 2022-01-12 [3487dfe] * update the example for the polarized MCX
- 2021-12-15 [b9e046a] fix out of bounds error due to limited precision of R_PI
- 2021-12-15 [3b10265] fix the built-in example to match the update in e5dfd78f28f31d710e02206cb2835aabcd4d5508
- 2021-12-15 [dbe17af] no Stoke vector output for unpolarized MCX simulation
- 2021-12-15 [99293dd] add sanity check for incident Stokes vector
- 2021-12-13 [f1537bd] no need to check constant memory usage in polarized mode
- 2021-12-13 [61281ae] use prop.g to return the anisotropy computed from Mie
- 2021-12-12 [3b0ecc0] fix #133, handling underflowing denorms in float to half conversion for muamus_float
- 2021-12-11 [979f691] Move scattering matrix from constant memory to global memory
- 2021-11-29 [5c13f4b] avoid divided by zero on windows cygwin gcc
- 2021-11-29 [ef57f4b] allow make double to compile
- 2021-11-28 [0c96fe8] accept JData styled NaN in the JSON input for srcdir
- 2021-11-26 [a4545a4] fix #131, mcxplotshapes now plots shapes with correct scale
- 2021-11-04 [2585471] making svmc matlab demos compatible with Octave 5
- 2021-11-03 [5976811] replace matlab-only function with more portable code
- 2021-11-01 [37e121c] update preprint version url
- 2021-10-21 [7a77bf7] * display rendering frame rate
- 2021-10-18 [99592c1] fix: #114 improve robustness to unknown boundry conditions
- 2021-10-14 [1aa2922] feat: Add one-sheet hyperboloid gaussian beam
- 2021-10-07 [86d56c2] feat: output prop. along with det. photon profile
- 2021-10-07 [24f4698] fix: ensure the largest grid to be accumulated
- 2021-10-06 [e5dfd78] feat: Support target mus or musp in polarized MCX
- 2021-10-06 [ae9216d] remove old det photon data after a new simulation
- 2021-10-05 [8cb21b5] support downloading detected photon data in mcxcloud
- 2021-10-04 [81ff4b1] fix rf replay bugs reported by Pauliina Hirvi
- 2021-09-24 [833bf6a] Reorganize some kernel code to optimize SVMC speed
- 2021-09-20 [605c15f] Fix numerical error of intersection test in SVMC
- 2021-09-20 [392ee87] Reorder code to fix photon detection for SVMC
- 2021-09-07 [5c44c6e] fix trajectory saving buffer length bug
- 2021-08-20 [99ea2b6] avoid continuous mua/mus inputs be treated as 0-label if mua=mus=0
- 2021-08-01 [943197a] Reorder preprocessing code to allow detector in SVMC Mask detector voxels only after the volume has been fully prepared!
- 2021-07-27 [65359f7] avoid extra level of square brackets for Optode.Detector
- 2021-07-27 [6b2f074] accept 3-element param1
- 2021-07-27 [2633bfb] avoid param1 missing error if not present
- 2021-07-23 [192613b] fix offset of cylinder along the axis direction, close #119
- 2021-07-17 [fc0465d] add tutorial 2 link
- 2021-07-17 [8c72d17] restore accidentally removed analytics tag
- 2021-07-13 [566df5e] provide flags to help access the detp.jdat file
- 2021-07-12 [b97c0f6] return metadata when loading simulation from library
- 2021-07-12 [3be6603] add default tab in the direct link,return mcx error if failed
- 2021-07-12 [70fb2a7] fix mixlabel byte order
- 2021-07-11 [24df0c3] add comment on raw voxel binary data layout
- 2021-07-07 [53d7ac0] fix shared mem misalignment error, close #118
- 2021-07-06 [7f8a2ac] allow ArrayZipSize to accept 1x2 array
- 2021-07-05 [9b00fa3] fix initial tab
- 2021-07-05 [2737853] deep copy data.options to avoid script error
- 2021-07-05 [0398b95] fix several bug while recording utorials
- 2021-07-04 [f5974ed] add preprint link
- 2021-07-04 [5d21a0d] create mcx cloud tutorial, add link
- 2021-07-03 [df0a48b] support tab param in url to open default tab
- 2021-07-03 [4c3f240] link json in direct link, remove schema
- 2021-07-03 [da6595e] add newline to json file download
- 2021-07-03 [cc3c06d] fix X/Y/ZSlabs parsing, restore original schemas
- 2021-07-03 [7c5054d] internal normalization of srcdir
- 2021-07-02 [877e9fe] get user id and group at runtime
- 2021-07-02 [22d6ffe] fix X/Y/ZSlabs schemas
- 2021-07-01 [270bb7c] prepare for beta testing
- 2021-06-30 [4b6df28] rename mcxone to mcxcloud, add help info
- 2021-06-30 [66b77fb] partial fix of json2mcx.m conversion issues
- 2021-06-25 [c399efa] enable negative g value support
- 2021-06-23 [3c642ce] set maximum characters to read for fscanf, fix #117
- 2021-06-23 [606b3d1] handle empty detector array in json2mcx
- 2021-06-22 [b537143] give a warning if the output type is not jacobian in replay
- 2021-06-17 [363d2d8] support reading .jdat file for replay
- 2021-06-04 [7191ca3] make thumbnail the same size when updating
- 2021-06-04 [5a2e13e] add tab overflow control
- 2021-06-04 [59e6be2] layout adjustments
- 2021-06-04 [4b55c88] minor polishing
- 2021-06-03 [1c29578] fix regression
- 2021-06-03 [3aedee6] add LengthUnit, MediaFormat in schema, support number and string for DebugFlag/SaveDataMask
- 2021-06-03 [64c5dd0] fix unnecessary shared memory allocation related to invcdf
- 2021-06-03 [ebf1ea1] * support user-defined phase functions via cfg.invcdf, close #13
- 2021-06-03 [0731511] revert back to no restarting policy so that overtime jobs can be killed
- 2021-06-02 [4d2a891] process cache,fix fullname,fix job status,fix server-side limit,kill overtime job
- 2021-06-02 [a08d676] update the skinvessel benchmark
- 2021-06-02 [168db14] * feat: save Mie function outputs mus, g to a file
- 2021-06-02 [57a44c5] feat: Add anisotropy g as an output of Mie func.
- 2021-06-02 [7387394] finally fix crossdomain post, change jsonp to json,test simu lib edit
- 2021-06-01 [95c6e1d] test:use default BC for all polarizedMC benchmarks
- 2021-06-01 [98697de] Add a three-layer slab demo for polarizedMC
- 2021-06-01 [7d86804] Add visualization for polarized MC example(MATLAB)
- 2021-05-31 [7d82a51] fix: resolve valgrind complaint: uninit. values
- 2021-05-31 [d6c9743] Add outputs in mcx2json.m to support polarizedMC
- 2021-05-31 [5088678] Add an example for polarized MC
- 2021-05-31 [d3054fd] Add document for polarized MC in mcxlab
- 2021-05-31 [44e0e9c] fea: extend loadmch.m to load output Stokes vector
- 2021-05-31 [a51cb52] feat: support polarized MC in command line (JSON)
- 2021-05-30 [d7921fe] skip checklimit if json is directly loaded from lib
- 2021-05-30 [65870f5] gui fine adjustment,use hash to update runcount,enable restart on fail,permit mcxpub update
- 2021-05-30 [692adfb] fix broken link
- 2021-05-30 [7a159ab] merge css
- 2021-05-30 [55bb1ea] initial drag and drop support, not working
- 2021-05-30 [fc9ca38] add meta headers, other minor adjustments
- 2021-05-29 [96cf071] support embedding src pattern in the all-in-one json/jdata file
- 2021-05-28 [450462c] * Add document for functions used in polarized MC
- 2021-05-28 [cbc3340] Optimize Stokes vector computation
- 2021-05-28 [9bd2ce0] Remove redundant code in preprocessing
- 2021-05-28 [06a9c6b] fix: resolve nan results due to numerical error
- 2021-05-28 [d9d1d0a] rewrite some code to save computation
- 2021-05-28 [9195141] Add an example to show polarized photon simulation
- 2021-05-27 [2b87275] fix: rewrite code for better readability
- 2021-05-26 [d836c81] fix: correct formula for stokes parameter update
- 2021-05-25 [105d5a9] * feat: Add stokes parameter output in MCXLAB
- 2021-05-25 [87d8847] * feat: add polarized photon simulation
- 2021-05-23 [d398cc9] add simulation restrictions for initial public testing of mcx cloud
- 2021-05-23 [26536d3] feat: add preprocessing for polarized MC in mcxlab
- 2021-05-22 [f0975c5] * support ring/annulus shaped source via disk source
- 2021-05-21 [6ed9727] * support svmc in command line;add svmc example
- 2021-05-21 [3d0a793] reading 8-byte svmc volume format from input file
- 2021-05-20 [4010d99] move svmc repacking to mcx_preprocess
- 2021-05-20 [3214c1b] remove duplicated preprocessing codes in mcx and mcxlab,fix detbc bug in command line
- 2021-05-20 [54b0602] run batch of jobs in each call to fill all GPU devices
- 2021-05-20 [de9850c] * add -K short option and svmc mediatype
- 2021-05-19 [660a8b8] relocate db and workspace folder to non www-data accessible paths
- 2021-05-19 [64f3008] update acknowledgement list
- 2021-05-19 [c168a87] can update thumbnail, add credit links
- 2021-05-19 [b9361a1] update to mcxcloud scripts
- 2021-05-15 [cef630b] save volume in jdata format by default
- 2021-05-15 [c50f871] define Frequency in json file instead of Omega
- 2021-05-15 [b4e7b57] initial support RF mua Jacobian in replay, thx Ilkka Nissilä, formula based on Juha Heiskala PhD thesis p45
- 2021-05-10 [073b168] * mcxcloud initial preview at JBO hot-topics
- 2021-05-05 [5732e6a] update front and backends
- 2021-05-01 [ee3f88d] update and rename mcxcloudd and mcxserver.cgi
- 2021-05-01 [eac952d] fix cylinder schema, add footer
- 2021-04-14 [aaa1eab] add download, fix jsonp callback, render output volume
- 2021-04-10 [aaef1f3] draw 3d fluence,use orth camera,add cancel
- 2021-04-04 [4ab8105] add src rendering, fix material color
- 2021-04-02 [1ed5272] fix cylinder and layer object drawing bug
- 2021-04-02 [f4ba0b4] add md5 digest for each submitted json for cache lookup
- 2021-03-31 [2c55c3b] change basic tab name
- 2021-03-31 [3c466a1] now mcxcloud can render 3D volumes, float32 buffer only
- 2021-03-29 [b379b2b] initial support in rendering 3d volume, add schema to support jdata ND array
- 2021-03-28 [31345a1] support Domain.Volume to encode JData-formatted 3D array
- 2021-03-28 [9c2e8c7] rendering all shape types, bbx as dashed box,add tag based material color
- 2021-03-27 [9f6e82c] avoid repainting preview
- 2021-03-26 [5109d29] add normal material, add box, subgrid and cylinder
- 2021-03-25 [ad0b814] draw grid from Domain.Dim
- 2021-03-25 [9b0cf95] fine tune fonts, add big tab initial screen, add svg background, add funding info
- 2021-03-25 [77f8f7a] add three.js for 3d preview
- 2021-03-24 [4274c77] rename mcxcloud.txt to mcxcloud
- 2021-03-24 [dc25a87] * add mcx cloud service server and client files, partially working
- 2021-03-22 [f9bc07c] use tabs in mcxone, add jquery by default
- 2021-03-18 [d8b88e1] fix unwanted double-precision math functions
- 2021-03-11 [f6ce5bd] update variable and function name to follow the convention
- 2021-03-11 [ca2ce60] add example: comparison of surface diffuse reflectance between MC and Diffusion
- 2021-03-05 [bcbb324] change window sizes using 96dpi default setting
- 2021-03-05 [5c8d27f] fix Name shape object schema
- 2021-03-03 [02add69] * MCX json schema and json editor are working, added more Shapes objects
- 2021-03-01 [940d725] wrapping up json input import feature in mcxstudio
- 2021-02-28 [64d629c] parse src/detector, media and shape
Updates since v2020:
- 2021-02-27 [a3b8457] * open/import JSON input file in MCX Studio
- 2021-01-07 [9811c83] reorder the input data layout to match the change in preprocessing
- 2020-10-22 [991910e] add function comment and revert unnecessary changes
- 2020-10-22 [3343338] * add benchmarks from SVMC paper to mcxlab
- 2020-10-19 [de87cbf] resolve code alignment issue
- 2020-10-18 [5acd287] fix photon detection issue for SVMC mode
- 2020-10-18 [61dbf63] fix ray-tracing issue after the initial template implementation
- 2020-10-17 [fbb4f8c] initial implementation of template for SVMC mode
- 2020-10-08 [dad83c6] resolve conflict between two branches to elimate mismatch in demo_focus_mirror_bc.m
- 2020-10-08 [fb61782] * sync master branch into nuvox(SVMC) branch
- 2020-09-20 [75f08c5] remove empty depends
- 2020-09-20 [fa98229] fix incorrect dependency
- 2020-09-20 [d748d29] add octave package files for mcxlab and mcxtools
- 2020-09-16 [cf3b1f0] fix typo, change default exe path
- 2020-09-16 [15e9946] * fix warnings found by debian packaging at https://mentors.debian.net/package/mcx/
- 2020-09-16 [04bb0e7] add man pages for other binaries
- 2020-09-14 [aca9f97] remove additional debian packging warnings
- 2020-09-14 [ce4e341] add desktop icon files
- 2020-09-14 [eb0aa9f] allow new lines in string values in json
- 2020-09-14 [4b1301a] set default exe folder to /usr/libexec, fall back to ~/bin/
- 2020-09-14 [643e4a1] * add photon as unified cmd for mcx/mcxcl/mmc,polish for debian packaging
- 2020-09-14 [a67bc6d] updates to ease debian packaging
- 2020-09-08 [8983305] Inno Installer Setup paths and file details fixed
- 2020-09-07 [a6bc5a9] another attempt to fix #105
- 2020-09-07 [ca303dd] change default shortcut group name, fix #105
- 2020-09-06 [0313d4c] install mcxstudio to 64bit folder, close #105
- 2020-09-04 [37b4914] add demo script for mirror bc
- 2020-09-04 [e561890] make mcxplotvol work in matlab 2010 or earlier
- 2020-09-04 [9518cfa] handle mirror bc correctly, close #104
- 2020-09-04 [64896aa] * reset pattern center position following a failed launch, fix #103
- 2020-09-02 [5af2e76] fix -geometry 0x0 error, see https://forum.lazarus.freepascal.org/index.php?topic=40593.0
- 2020-09-01 [dd4be78] add cubesph60b to match example/benchmark2
- 2020-08-30 [971ffac] fix extended ascii letters
- 2020-08-29 [6eb9596] update mcxcreate.m, add mcxplotshapes.m to render json shapes
- 2020-08-29 [0199dad] clean up code and add comments for SVMC
- 2020-08-29 [94d55a7] * add mcxcreate, force mcxlab return one output
- 2020-08-28 [d917751] give an error for unsupported single dash option
- 2020-08-28 [093c9ba] * add pre-processing for SVMC mode
- 2020-08-28 [a79e116] add mode delphi in carbon unit
- 2020-08-27 [63e5a5f] handle det radii less than or equal to 0.5, fix #101
- 2020-08-27 [8f93ee2] fix make mex link error
- 2020-08-26 [65f0fe4] fix issrcfrom0 offset
- 2020-08-26 [79f9d70] * multiply voxelsize with det radius
- 2020-08-26 [d5c3c11] fix mcxpreview det radis issue, require srcpos and tend in mcxlab
- 2020-08-24 [1af5507] avoid error on mac
- 2020-08-24 [2fce8e5] add missing carbon unit for mac
- 2020-08-24 [6f11857] add command line option cheatsheet
- 2020-08-24 [5046de0] fix cmake command
- 2020-08-24 [cea663b] test cmake in travis
- 2020-08-24 [782b4a3] massive update of documentation
- 2020-08-24 [041e386] massive update to README to describe all output formats
Between 2020 and 2023, seven new journal papers have been published as the result of this project, including [Yan2020]. Please see the full list at http://mcx.space/#publication
- [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.
- [Fang2022] Qianqian Fang, Shijie Yan, "MCX Cloud—a modern, scalable, high-performance and in-browser Monte Carlo simulation platform with cloud computing," J. Biomed. Opt. 27(8) 083008, 2022
- [Yan2022] Shijie Yan, Steven L. Jacques, Jessica C. Ramella-Roman, Qianqian Fang, "Graphics processing unit-accelerated Monte Carlo simulation of polarized light in complex three-dimensional media," J. of Biomedical Optics, 27(8), 083015 (2022)
- [Zhang2022] Yuxuang Zhang, Qianqian Fang, "BlenderPhotonics – an integrated open-source software environment for three-dimensional meshing and photon simulations in complex tissues," J. of Biomedical Optics, 27(8), 083014 (2022)
- [RaayaiArdakani2022] Matin Raayai Ardakani, Leiming Yu, David R. Kaeli, Qianqian Fang, "Framework for Denoising Monte Carlo Photon Transport Simulations Using Deep Learning," J Biomed Opt. 2022 May;27(8):083019. doi: 10.1117/1.JBO.27.8.083019
- [Yuan2021] Yaoshen Yuan, Shijie Yan, and Qianqian Fang*, "Light transport modeling in highly complex tissues using the implicit mesh-based Monte Carlo algorithm," Biomed. Optics Express, 12(1), 147-161, (2021)
- [Hirvi2023] Hirvi P, Kuutela T, Fang Q, Hannukainen A, Hyvonen N, Nissilä I. Effects of atlas-based anatomy on modelled light transport in the neonatal head. Phys Med Biol. 2023 May 11. doi: 10.1088/1361-6560/acd48c. PMID: 37167982.
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:
http://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 http://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 graphics card that MCX supports is the Fermi series (circa 2010). Using the latest NVIDIA card is expected to produce the best speed. You must have a fermi (GTX 4xx) or newer (5xx/6xx/7xx/9xx/10xx/20xx/30xx series) graphics card. The default release of MCX supports atomic operations and photon detection. 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 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. MCX does not require double-precision support in your hardware.
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
http://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
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) 2009-2023 Qianqian Fang <q.fang at neu.edu> #
# https://mcx.space/ & https://neurojson.org/ #
# #
# 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 consider using JSON (https://neurojson.org/) for your research data #
###############################################################################
$Rev::ffc8ab$ v2023 $Date::2023-09-13 12:24:07 -04$ 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 [0|int|mch](--seed) set random-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
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:0-65535 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
-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://github.com/NeuroJSON/jnifti,
MATLAB/Octave: JSONLab toolbox https://github.com/NeuroJSON/jsonlab,
Python: PyJData: https://pypi.org/project/jdata
JavaScript: JSData: https://github.com/NeuroJSON/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
--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 OpenJData initiative (http://openjdata.org) led by the MCX author Dr. Qianqian Fang. The OpenJData 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 http://openjdata.org/wiki
Specifically, to parse/save .jnii files in MATLAB, you should use
- JSONLab for MATLAB (https://github.com/fangq/jsonlab) or install
octave-jsonlabon Fedora/Debian/Ubuntu jsonencode/jsondecodein MATLAB +jdataencode/jdatadecodefrom JSONLab (https://github.com/fangq/jsonlab)
To parse/save .jnii files in Python, you should use
- PyJData module (https://pypi.org/project/jdata/) 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 OpenJData 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 http://openjdata.org/wiki
Specifically, to parse/save .jnii files in MATLAB, you should use one of
- JSONLab for MATLAB (https://github.com/fangq/jsonlab) or install
octave-jsonlabon Fedora/Debian/Ubuntu jsonencode/jsondecodein MATLAB +jdataencode/jdatadecodefrom JSONLab (https://github.com/fangq/jsonlab)
To parse/save .jnii files in Python, you should use
- PyJData module (https://pypi.org/project/jdata/) 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
int reserved[2]; // 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://github.com/fangq/jsonlab) or install
octave-jsonlabon Fedora/Debian/Ubuntu jsonencode/jsondecodein MATLAB +jdataencode/jdatadecodefrom JSONLab (https://github.com/fangq/jsonlab)
For Python
- PyJData module (https://pypi.org/project/jdata/) 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:
A dedicated GPU is a GPU that is not connected to a monitor. If you use a
non-dedicated GPU, any kernel (GPU function) can not run more than a few
seconds. This greatly limits the efficiency of MCX. To set up a dedicated GPU,
it is suggested to install two graphics cards on your computer, one is set up
for displays, the other one is used for GPU computation only. If you have a
dual-GPU card, you can also connect one GPU to a single monitor, and use the
other GPU for computation (selected by -G in mcx). If you have to use a
non-dedicated GPU, you can either use the pure command-line mode (for Linux,
you need to stop X server), or use the -r flag to divide the total
simulation into a set of simulations with less photons, so that each simulation
only lasts a few seconds.
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: 2019-2020 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, anders@cb.uu.se
- 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 wenzel.jakob@epfl.ch
- 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] http://iso2mesh.sourceforge.net/cgi-bin/index.cgi?jsonlab
- [5] http://science.jrank.org/pages/60024/particle-fluence.html
- [6] http://www.opticsinfobase.org/oe/abstract.cfm?uri=oe-17-22-20178