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Biomedical Optics & Medical Imaging

Online Monte Carlo for biomedical optics

Generalizing a model of photon migration that uses random values to predict outcomes provides a tool well suited to a wide range of minimally invasive diagnostics.
7 November 2011, SPIE Newsroom. DOI: 10.1117/2.1201110.003879

Optical and laser diagnostics are widely used in a number of applications, including cancer research, vascular and developmental biology, dermatology, pharmacy, materials sciences, food, and the cosmetic and health care industries. Optical techniques provide a broad variety of practical solutions for non-invasive diagnostics in a range of studies from single cells to the biopsy of specific biological tissues and whole organs.

Conceptual design of a particular optical diagnostic system for non-invasive in vivo measurements of structural alterations of biological tissues and changes in their physiological properties requires careful selection of various technical parameters, including wavelength, coherence, polarization and intensity profile of incident optical radiation, sensitivity of the detector, size, and the geometry and mutual position of source and detector. Owing to a range of probing conditions and the complex composite structure of biological tissues, no general analytical solution exists that can describe a detected optical signal and how it is affected by structural or physiological changes. Consequently, stochastic Monte Carlo (MC) modeling, which predicts different outcomes based on random number generation, is used to imitate light propagation within the complex tissue-like turbid media.

An opportunity to directly simulate the influence of varieties of biological tissues on the probing light makes MC a primary tool in biomedical optics and optical engineering. Integrated into a computational model of human skin, for example, MC has been used to simulate reflectance spectra, to imitate 2D images provided by optical coherence tomography (OCT), and to study the coherent effects of multiple light scattering and optical clearing. Nevertheless, the diversity of optical and laser diagnostic techniques based on different properties of light and mechanisms of light-tissue interactions require that a fresh MC code be developed for each new application.

To generalize MC modeling for multipurpose use, we applied the concept of object-oriented programming (OOP).2 In particular, OOP allows the user to describe photon migration and light-tissue interaction as interacting objects. The photon object propagates through the object medium and interacts with its constituents, such as cells, blood vessels, collagen fiber, and tumors. Dividing up the medium into objects makes it possible to develop realistic tissue models presenting 3D spatial variations of biological structure. Moreover, defining the actual structure of biological tissue as an object enables it to be imported into the model from any of several imaging modalities, such as electron microscopy, magnetic resonance imaging, and OCT.

We used the inheritance feature of OOP to create a ‘smart’ hierarchical structure of the MC code to avoid having multiple classes for similar tasks. The hierarchy allows ‘allied’ objects to share variables and members, significantly reducing the amount of source code and paving the way to extend and generalize the MC. Depending on the application, objects can be tuned to an appropriate state of light-tissue interaction and to a particular optical diagnostic technique.

To achieve optical simulation performance, we employed a recently developed parallel computing framework known as Compute Unified Device Architecture (CUDA), introduced by the NVIDIA Corporation. Specially designed for professional 3D graphics applications, this technology allows each graphic chip to be logically divided into hundreds of cores, turning the graphics processing unit into a massive co-processor for parallel computations. This capability enables simulation of thousands of objects—i.e., simultaneous propagation of photons in the medium—that speeds the process of simulation up to 103 times.

Integrating CUDA with modern web-based technologies, we created an on-line MC computational tool to suit the needs of biophotonics and biomedical optics.1 Figure 1 shows the interactive user interface for selecting a particular MC application. We used Microsoft Silverlight, ASP (Active Server Pages), and .NET Framework technologies to create a cross-platform lightweight interactive interface to access a particular MC application and to meet modern web-design and security requirements, respectively.

Figure 1. The interactive front end of the Monte Carlo Online computational tool presents a selection of applications.1 OCT: Optical coherence tomography.

Monte Carlo Online can be used free of charge to imitate optical radiation propagation within complex multi-layered media such as biological tissues. The current version provides access to simulation of detector depth sensitivity (sampling volume) for a range of probes typically used in reflectance-based measurements, reflectance spectra of human skin and/or multi-layered scattering structures, and skin-color modeling. The tool allows users to customize the parameters of the medium, probe, and observation area. In future, we plan to provide monthly updates of additional MC developments, including fluence rate counter, fluorescence spectra simulation, speckles and laser Doppler blood-flow assessment, OCT images, laser pulse propagation, and image transfer.

Igor Meglinski, Alexander Doronin
University of Otago
Dunedin, New Zealand

Igor Meglinski obtained his PhD in biophysics and biomedical optics jointly from the University of Pennsylvania, US, and Saratov State University, Russia (1997). He is currently head of Biophotonics and Biomedical Imaging at the Department of Physics, University of Otago.

Alex Doronin is a PhD candidate in the Biophotonics and Biomedical Imaging research group. He focuses on developing high-performance computing systems and algorithm optimization techniques for biomedical and optical diagnostics, light-tissue interaction, OCT, polarization-sensitive OCT, Doppler OCT, and image processing.

1. http://biophotonics.otago.ac.nz/MCOnline.aspx Monte Carlo Online pilot version website. Accessed 25 September 2011.
2. A. Doronin, I. Meglinski, Online object oriented Monte Carlo computational tool for the needs of biomedical optics, Biomed. Opt. Express 2, no. 9, pp. 2461-2469, 2011. doi:10.1364/BOE.2.002461