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Dear colleagues,

Thanks for being a registered MCX/MMC/MCX-CL user and your support of our Monte Carlo modeling software. It is our great pleasure to let you know that we have put together a new release, v2019.3, for MCX, MMC and MCX-CL. As a registered user, you can directly access these new packages from the below URL

https://sourceforge.net/projects/mcx/files/

We highly recommend downloading the all-in-one package, MCXStudio, to ease installation - it contains all updated software modules including MCX/MMC/MCXCL/MCXLAB/MMCLAB/MCXLABCL.

All released packages have packed significant improvement of our modeling tools, both in functionality and usability, and are results of our continuous development of these utilities and novel MC algorithms.  Among many of the significant improvements, a subset is listed below

• A built-in OpenGL-based 3D volume renderer in MCXStudio
• Both MCX and MMC support "photon sharing" - simultaneous forward/Jacobian for many source patterns [presented at PW19]
• MCX supports 4 types of boundary conditions - total absorption, total reflection, Fresnel reflection and cyclic/periodic BCs, can be axis-plane specific
  
• Support isotropic launch of photons for all focusable sources, including pattern and pattern3d sources, by setting srcdir(4) to nan.
• Built-in MCX image denoiser [see our new paper Yuan2018] to improve image quality, equivalent to 3.5x more photons
• Next-gen MMC: DMMC or dual-grid MMC [see our new paper Yan2019] - an improved MMC combining mesh and grid - both faster and more accurate than the regular MMC
• Four built-in complex domain examples: colin27 atlas, usc-19.5 brain atlas, Digimouse and mcxyz skinvessel benchmark, ready to be adapted
• Added a series of MC data post-processing MATLAB functions in all tools to obtain detected photon weight, average pathlengths, TOF, TPFS etc.
  
• Provided independent visualization utilities mcxshow and mcxviedwer in addition to mcxstudio
• MCX/MCX-CL worked seamlessly on the latest generation NVIDIA and AMD GPUs - namely, Volta, Turing, and Vega II, see our speed benchmarks at http://mcx.space/gpubench/ and http://mcx.space/computebench/
  

For all released MCX tools, we have provided pre-compiled binaries and matlab mex files for 64bit Windows, MacOS and Linux platforms. These tools are designed to be highly portable - built to be both forward and backward compatible to nearly all generations of GPU/CPU hardware. You do not need to install special libraries (such as CUDA) because the libraries are already compiled into the binaries. In most cases, all you need is a properly installed graphics driver (which you already have).

To read more about this new release, please browse our detailed Release Notes on our Wiki:

http://mcx.space/wiki/?Get

If you have any questions, please feel free to direct those to our forum at

https://groups.google.com/forum/?hl=en#!forum/mcx-users

Enjoy the new software!

Qianqian Fang, PhD
Assistant Professor
Dept of Bioengineering, Northeastern University

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PS: if you wish to read more about the new algorithms implemented in this release, please check out our new papers online

Summary: Dual-grid MMC for faster and more accurate mesh-based simulations
Shijie Yan, Anh Phong Tran, Qianqian Fang*, “A dual-grid mesh-based Monte Carlo algorithm for efficient photon transport simulations in complex 3-D media,” J. of Biomedical Optics, 24(2), 020503 (2019).
Browse: https://doi.org/10.1117/1.JBO.24.2.020503

Summary: Denoising MC simulation using a noise-adaptive filter
Yaoshen Yuan, Leiming Yu, Zafer Doğan, Qianqian Fang, "Graphics processing units-accelerated adaptive nonlocal means filter for denoising three-dimensional Monte Carlo photon transport simulations," J. of Biomedical Optics, 23(12), 121618 (2018).
Browse: https://www.osapublishing.org/abstract.cfm?uri=OTS-2018-JTh3A.41

Summary: Photon-sharing for simultaneous pattern-based simulations
Ruoyang Yao, Shijie Yan, Xavier Intes, Qianqian Fang, "Accelerating Monte Carlo forward model with structured light illumination via 'photon sharing'," Photonics West 2019, paper#10874-11, San Francisco, CA, USA.
Browse: https://www.spiedigitallibrary.org/conference-presentations/10874/108740B/Accelerating-Monte-Carlo-forward-model-with-structured-light-illumination-via/10.1117/12.2510291?SSO=1

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