Project Summary/Abstract
Abstract: The rapid evolution of the field of biophotonics has produced numerous emerging techniques for
combatting diseases and addressing urgent human health challenges, offering safe, non-invasive, and portable
light-based diagnostic and therapeutic methods, and attracting exponentially growing attention over the past
decade. Rigorous, fast, versatile and publicly available computational tools have played pivotal roles in
the success of these novel approaches, leading to breakthroughs in new instrumentation designs and
extensive explorations of complex biological systems such as human brains. The Monte Carlo eXtreme (MCX,
http://mcx.space) light transport simulation platform developed by our team has become one of the most widely
disseminated biophotonics modeling platforms, known for its high accuracy, high speed and versatility, as
attested to by its over 27,000 downloads and nearly 1,000 citations from a large (2,400+ registered users)
world-wide user community. Over the past years, we have also been pushing the boundaries in cutting-edge
Monte Carlo (MC) photon simulation algorithms by exploring modern GPU architectures, advanced anatomical
modeling methods and systematic software optimizations. In this proposed project, we will build upon the
strong momentum created in the initial funding period, and strive to further advance the state-of-the-art of
GPU-accelerated MC light transport modeling with strong support from the world’s leading GPU manufacturers
and experts, further expanding our platform to address a number of emerging challenges in biomedical optics
applications. Specifically, we will further explore emerging GPU architecture and resources, such as ray-
tracing cores, half- and mixed-precision hardware, and portable programming models, to further accelerate the
MC modeling speed. We will also develop hybrid shape/mesh-based MC algorithms to dramatically advance
the capability in simulating extremely complex yet realistic anatomical structures, such as porous tissues in the
lung, dense vessel networks in the brain, and multi-scaled tissue domains. In parallel, we aim to make a break-
through in applying deep-learning-based image denoising techniques to equivalently accelerate MC
simulations by 2 to 3 orders of magnitudes, as suggested in our preliminary studies. In the continuation of this
project, we strive to create a dynamic and community-engaging simulation environment by extending our
software to allow users to create, share, browse, and reuse pre-configured simulations, avoiding
redundant works in re-creating complex simulations and facilitating reproducible research. In addition, we will
expand our well-received user training programs and widely disseminate our open-source tools via major Linux
distributions and container images. At the end of this continued funding period, we will provide the community
with a significantly accelerated, widely-available and well-supported biophotonics modeling platform that
can handle multi-scaled tissue optical modeling ranging from microscopic to macroscopic domains.