Next-generation Monte Carlo eXtreme Light Transport Simulation Platform

项目摘要/摘要 摘要：生物光子学领域的快速发展产生了许多新兴技术 抗击疾病和应对紧迫的人类健康挑战，提供安全、非侵入性和便携 基于光的诊断和治疗方法，在过去引起了指数级的关注 十年。严格、快速、通用和公开可用的计算工具在以下方面发挥了关键作用 这些新方法的成功，导致了新仪器设计和 广泛探索复杂的生物系统，如人脑。蒙特卡罗极值(MCx， 我们团队开发的http://mcx.space)光传输仿真平台已经成为应用最广泛的平台之一 分散式生物光子学建模平台，以其高精度、高速度和多功能性而闻名，如 其超过27,000次的下载量和来自大型(2,400名注册用户)的近1,000次引用证明了这一点 全球用户社区。在过去的几年里，我们也一直在尖端领域突破界限 蒙特卡罗(MC)光子模拟算法通过探索现代GPU架构、高级解剖学 建模方法和系统的软件优化。在这个拟议的项目中，我们将在 在最初的资助期创造了强劲的势头，并努力进一步推进 GPU加速的MC光传输建模，得到了全球领先的GPU制造商的大力支持 和专家，进一步扩展我们的平台，以应对生物医学光学领域的许多新挑战 申请。具体地说，我们将进一步探索新兴的GPU架构和资源，如ray- 跟踪核心、半精度和混合精度硬件以及可移植编程模型，以进一步加速 MC建模速度。我们还将开发基于形状/网格的混合MC算法，以显著提高 能够模拟极其复杂但逼真的解剖结构，例如 肺，大脑中致密的血管网络，以及多尺度的组织域。同时，我们的目标是取得突破- 通过应用基于深度学习的图像去噪技术等效加速MC 模拟2到3个数量级，正如我们的初步研究所建议的那样。在继续这一过程中 项目，我们努力创建一个动态和社区参与的模拟环境，通过扩展我们的 允许用户创建、共享、浏览和重复使用预先配置的模拟的软件，从而避免 在重新创建复杂的模拟和促进可重复研究方面的多余工作。此外，我们还将 扩大我们广受欢迎的用户培训计划，并通过主要的Linux广泛传播我们的开源工具 分发和容器图像。在这个持续的资助期结束时，我们将为社会提供 具有显著加速、广泛使用和良好支持的生物光子学建模平台， 可以处理从微观领域到宏观领域的多尺度组织光学建模。

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.