Novel biophotonics techniques continue to emerge at an unprecedented pace to address the rapidly expanding clinical needs for accurate, fast, non- or minimally-invasive quantification of physiology. A thorough understanding of the complex interaction between photons and biological tissues is at the very foundation of these techniques. Over the past 5 years, our group has been dedicated to the development of computationally efficient Monte Carlo (MC) methods for modeling light transport inside complex tissue structures. As a result of this research, two open-source simulation packages - Monte Carlo extreme (MCX) and Mesh-based Monte Carlo (MMC) - have been developed and disseminated. They are now driving research in many optics labs across the world as well as in small businesses, attested to by over 240 citations, more than 9,000 downloads and nearly 20,000 unique web visitors worldwide. In this proposal, we seek to further extend, solidify, and disseminate MCX and MMC, following the specific guidelines in NIH PA-14-156. Novel MC- based algorithms for modeling wide-field illumination and detection, rapid tomographic reconstructions and further speed acceleration will be developed and implemented to meet the escalating needs throughout the community towards developing the next-generation optical imaging techniques. As experienced open-source developers and maintainers, we embrace the pivotal role of engagement with our user community and plan to significantly enhance our software user experience, user support and systematic outreach and training. Towards significantly improved usability and scalability, we will develop web-based MCX/MMC to make our platform widely accessible, and a distributed computing cloud to enable fast processing of large, complex multi-modal datasets. We will also develop formal training courses and materials to better educate researchers, strengthen the support and feedback mechanisms, and work to create a standardized optical imaging data- exchange specification through our platform. Accomplishing these goals will not only make MCX/MMC one of the most accurate, efficient and comprehensive optical modeling platforms available, but also will set a new standard in our community for developing innovative biophotonics techniques, exploring complex biological systems, facilitating reproducible research and forging efficient collaboration among a broad research community.
By further extending, solidifying and disseminating our widely distributed GPU-accelerated Monte Carlo light transport modeling platform, we aim to make a broad impact and set a new standard in our community for developing innovative biophotonics techniques, exploring complex biological systems, facilitating reproducible research and promoting efficient collaboration among the research community.
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|Horn, Andreas; Reich, Martin; Vorwerk, Johannes et al. (2017) Connectivity Predicts deep brain stimulation outcome in Parkinson disease. Ann Neurol 82:67-78|
|Verleker, Akshay Prabhu; Shaffer, Michael; Fang, Qianqian et al. (2017) Optical dosimetry probes to validate Monte Carlo and empirical-method-based NIR dose planning in the brain: publisher's note. Appl Opt 56:1131|
|Yao, Ruoyang; Intes, Xavier; Fang, Qianqian (2016) Generalized mesh-based Monte Carlo for wide-field illumination and detection via mesh retessellation. Biomed Opt Express 7:171-84|
|Deng, Bin; Brooks, Dana H; Boas, David A et al. (2015) Characterization of structural-prior guided optical tomography using realistic breast models derived from dual-energy x-ray mammography. Biomed Opt Express 6:2366-79|