FullMonteCUDA: a fast, flexible, and accurate GPU-accelerated Monte Carlo simulator for light propagation in turbid media

Asymmetric imaging through engineered Janus particle obscurants using a Monte Carlo approach for highly asymmetric scattering media

The advancement of imaging systems has significantly ameliorated various technologies, including Intelligence Surveillance Reconnaissance Systems and Guidance Systems, by enhancing target detection, recognition, identification, positioning, and tracking capabilities. These systems can be countered by deploying obscurants like smoke, dust, or fog to hinder visibility and communication. However, these counter-systems affect the visibility of both sides of the cloud. In this sense, this manuscript introduces a new concept of a smoke cloud composed of engineered Janus particles to conceal the target image on one side while providing clear vision from the other. The proposed method exploits the unique scattering properties of Janus particles, which selectively interact with photons from different directions to open up the possibility of asymmetric imaging. This approach employs a model that combines a genetic algorithm with Discrete Dipole Approximation to optimize the Janus particles' geometrical parameters for the desired scattering properties. Moreover, we propose a Monte Carlo-based approach to calculate the image formed as photons pass through the cloud, considering highly asymmetric particles, such as Janus particles. The effectiveness of the cloud in disguising a target is evaluated by calculating the Probability of Detection (PD) and the Probability of Identification (PID) based on the constructed image. The optimized Janus particles can produce a cloud where it is possible to identify a target more than 50% of the time from one side (PID > 50%) while the target is not detected more than 50% of the time from the other side (PD < 50%). The results demonstrate that the Janus particle-engineered smoke enables asymmetric imaging with simultaneous concealment from one side and clear visualization from the other. This research opens intriguing possibilities for modern obscurant design and imaging systems through highly asymmetric and inhomogeneous particles besides target detection and identification capabilities in challenging environments.

Integrating clinical access limitations into iPDT treatment planning with PDT-SPACE

PDT-SPACE is an open-source software tool that automates interstitial photodynamic therapy treatment planning by providing patient-specific placement of light sources to destroy a tumor while minimizing healthy tissue damage. This work extends PDT-SPACE in two ways. The first enhancement allows specification of clinical access constraints on light source insertion to avoid penetrating critical structures and to minimize surgical complexity. Constraining fiber access to a single burr hole of adequate size increases healthy tissue damage by 10%. The second enhancement generates an initial placement of light sources as a starting point for refinement, rather than requiring entry of a starting solution by the clinician. This feature improves productivity and also leads to solutions with 4.5% less healthy tissue damage. The two features are used in concert to perform simulations of various surgery options of virtual glioblastoma multiforme brain tumors.

A Monte Carlo simulation for Moving Light Source in Intracavity PDT

We developed a simulation method for modeling the light fluence delivery in intracavity Photodynamic Therapy (icav-PDT) for pleural lung cancer using a moving light source. Due to the large surface area of the pleural lung cavity, the light source needs to be moved to deliver a uniform dose around the entire cavity. While multiple fixed detectors are used for dosimetry at a few locations, an accurate simulation of light fluence and fluence rate is still needed for the rest of the cavity. We extended an existing Monte Carlo (MC) based light propagation solver to support moving light sources by densely sampling the continuous light source trajectory and assigning the proper number of photon packages launched along the way. The performance of Simphotek GPU CUDA-based implementation of the method - PEDSy-MC - has been demonstrated on a life-size lung-shaped phantom, custom printed for testing icav-PDT navigation system at the Perlman School of Medicine (PSM) - calculations completed under a minute (for some cases) and within minutes have been achieved. We demonstrate results within a 5% error of the analytic solution for multiple detectors in the phantom. PEDSy-MC is accompanied by a dose-cavity visualization tool that allows real-time inspection of dose values of the treated cavity in 2D and 3D, which will be expanded to ongoing clinical trials at PSM. PSM has developed a technology to measure 8-detectors in a pleural cavity phantom using Photofrin-mediated PDT that has been used during validation.

Efficient computation of the steady-state and time-domain solutions of the photon diffusion equation in layered turbid media

Accurate and efficient forward models of photon migration in heterogeneous geometries are important for many applications of light in medicine because many biological tissues exhibit a layered structure of independent optical properties and thickness. However, closed form analytical solutions are not readily available for layered tissue-models, and often are modeled using computationally expensive numerical techniques or theoretical approximations that limit accuracy and real-time analysis. Here, we develop an open-source accurate, efficient, and stable numerical routine to solve the diffusion equation in the steady-state and time-domain for a layered cylinder tissue model with an arbitrary number of layers and specified thickness and optical coefficients. We show that the steady-state ([Formula: see text] ms) and time-domain ([Formula: see text] ms) fluence (for an 8-layer medium) can be calculated with absolute numerical errors approaching machine precision. The numerical implementation increased computation speed by 3 to 4 orders of magnitude compared to previously reported theoretical solutions in layered media. We verify our solutions asymptotically to homogeneous tissue geometries using closed form analytical solutions to assess convergence and numerical accuracy. Approximate solutions to compute the reflected intensity are presented which can decrease the computation time by an additional 2-3 orders of magnitude. We also compare our solutions for 2, 3, and 5 layered media to gold-standard Monte Carlo simulations in layered tissue models of high interest in biomedical optics (e.g. skin/fat/muscle and brain). The presented routine could enable more robust real-time data analysis tools in heterogeneous tissues that are important in many clinical applications such as functional brain imaging and diffuse optical spectroscopy.

Photodynamic therapy in a pleural cavity using monte carlo simulations with 2D/3D Graphical Visualization

Cancer therapy using Photodynamic Therapy (PDT) has been investigated for some time [1,2] and now it is a growing area of interest in clinical trials [3]. Monte Carlo (MC) simulations were used for early laboratory studies [4,5] for analysis in PDT. Various improvements in the MC method have advanced the field in recent years.

Meshless Monte Carlo radiation transfer method for curved geometries using signed distance functions

SIGNIFICANCE

Monte Carlo radiation transfer (MCRT) is the gold standard for modeling light transport in turbid media. Typical MCRT models use voxels or meshes to approximate experimental geometry. A voxel-based geometry does not allow for the precise modeling of smooth curved surfaces, such as may be found in biological systems or food and drink packaging. Mesh-based geometry allows arbitrary complex shapes with smooth curved surfaces to be modeled. However, mesh-based models also suffer from issues such as the computational cost of generating meshes and inaccuracies in how meshes handle reflections and refractions.

AIM

We present our algorithm, which we term signedMCRT (sMCRT), a geometry-based method that uses signed distance functions (SDF) to represent the geometry of the model. SDFs are capable of modeling smooth curved surfaces precisely while also modeling complex geometries.

APPROACH

We show that using SDFs to represent the problem's geometry is more precise than voxel and mesh-based methods.

RESULTS

sMCRT is validated against theoretical expressions, and voxel and mesh-based MCRT codes. We show that sMCRT can precisely model arbitrary complex geometries such as microvascular vessel network using SDFs. In comparison with the current state-of-the-art in MCRT methods specifically for curved surfaces, sMCRT is more precise for cases where the geometry can be defined using combinations of shapes.

CONCLUSIONS

We believe that SDF-based MCRT models are a complementary method to voxel and mesh models in terms of being able to model complex geometries and accurately treat curved surfaces, with a focus on precise simulation of reflections and refractions. sMCRT is publicly available at https://github.com/lewisfish/signedMCRT.

Convolutional neural network-based common-path optical coherence tomography A-scan boundary-tracking training and validation using a parallel Monte Carlo synthetic dataset

We present a parallel Monte Carlo (MC) simulation platform for rapidly generating synthetic common-path optical coherence tomography (CP-OCT) A-scan image dataset for image-guided needle insertion. The computation time of the method has been evaluated on different configurations and 100000 A-scan images are generated based on 50 different eye models. The synthetic dataset is used to train an end-to-end convolutional neural network (Ascan-Net) to localize the Descemet's membrane (DM) during the needle insertion. The trained Ascan-Net has been tested on the A-scan images collected from the ex-vivo human and porcine cornea as well as simulated data and shows improved tracking accuracy compared to the result by using the Canny-edge detector.

Framework for denoising Monte Carlo photon transport simulations using deep learning

SIGNIFICANCE

The Monte Carlo (MC) method is widely used as the gold-standard for modeling light propagation inside turbid media, such as human tissues, but combating its inherent stochastic noise requires one to simulate a large number photons, resulting in high computational burdens.

AIM

We aim to develop an effective image denoising technique using deep learning (DL) to dramatically improve the low-photon MC simulation result quality, equivalently bringing further acceleration to the MC method.

APPROACH

We developed a cascade-network combining DnCNN with UNet, while extending a range of established image denoising neural-network architectures, including DnCNN, UNet, DRUNet, and deep residual-learning for denoising MC renderings (ResMCNet), in handling three-dimensional MC data and compared their performances against model-based denoising algorithms. We also developed a simple yet effective approach to creating synthetic datasets that can be used to train DL-based MC denoisers.

RESULTS

Overall, DL-based image denoising algorithms exhibit significantly higher image quality improvements over traditional model-based denoising algorithms. Among the tested DL denoisers, our cascade network yields a 14 to 19 dB improvement in signal-to-noise ratio, which is equivalent to simulating 25  ×   to 78  ×   more photons. Other DL-based methods yielded similar results, with our method performing noticeably better with low-photon inputs and ResMCNet along with DRUNet performing better with high-photon inputs. Our cascade network achieved the highest quality when denoising complex domains, including brain and mouse atlases.

CONCLUSIONS

Incorporating state-of-the-art DL denoising techniques can equivalently reduce the computation time of MC simulations by one to two orders of magnitude. Our open-source MC denoising codes and data can be freely accessed at http://mcx.space/.

MCDataset: a public reference dataset of Monte Carlo simulated quantities for multilayered and voxelated tissues computed by massively parallel PyXOpto Python package

SIGNIFICANCE

Current open-source Monte Carlo (MC) method implementations for light propagation modeling are many times tedious to build and require third-party licensed software that can often discourage prospective researchers in the biomedical optics community from fully utilizing the light propagation tools. Furthermore, the same drawback also limits rigorous cross-validation of physical quantities estimated by various MC codes.

AIM

Proposal of an open-source tool for light propagation modeling and an easily accessible dataset to encourage fruitful communications amongst researchers and pave the way to a more consistent comparison between the available implementations of the MC method.

APPROACH

The PyXOpto implementation of the MC method for multilayered and voxelated tissues based on the Python programming language and PyOpenCL extension enables massively parallel computation on numerous OpenCL-enabled devices. The proposed implementation is used to compute a large dataset of reflectance, transmittance, energy deposition, and sampling volume for various source, detector, and tissue configurations.

RESULTS

The proposed PyXOpto agrees well with the original MC implementation. However, further validation reveals a noticeable bias introduced by the random number generator used in the original MC implementation.

CONCLUSIONS

Establishing a common dataset is highly important for the validation of existing and development of MC codes for light propagation in turbid media.

BlenderPhotonics: an integrated open-source software environment for three-dimensional meshing and photon simulations in complex tissues

SIGNIFICANCE

Rapid advances in biophotonics techniques require quantitative, model-based computational approaches to obtain functional and structural information from increasingly complex and multiscaled anatomies. The lack of efficient tools to accurately model tissue structures and subsequently perform quantitative multiphysics modeling greatly impedes the clinical translation of these modalities.

AIM

Although the mesh-based Monte Carlo (MMC) method expands our capabilities in simulating complex tissues using tetrahedral meshes, the generation of such domains often requires specialized meshing tools, such as Iso2Mesh. Creating a simplified and intuitive interface for tissue anatomical modeling and optical simulations is essential toward making these advanced modeling techniques broadly accessible to the user community.

APPROACH

We responded to the above challenge by combining the powerful, open-source three-dimensional (3D) modeling software, Blender, with state-of-the-art 3D mesh generation and MC simulation tools, utilizing the interactive graphical user interface in Blender as the front-end to allow users to create complex tissue mesh models and subsequently launch MMC light simulations.

RESULTS

Here, we present a tutorial to our Python-based Blender add-on-BlenderPhotonics-to interface with Iso2Mesh and MMC, which allows users to create, configure and refine complex simulation domains and run hardware-accelerated 3D light simulations with only a few clicks. We provide a comprehensive introduction to this tool and walk readers through five examples, ranging from simple shapes to sophisticated realistic tissue models.

CONCLUSIONS

BlenderPhotonics is user friendly and open source, and it leverages the vastly rich ecosystem of Blender. It wraps advanced modeling capabilities within an easy-to-use and interactive interface. The latest software can be downloaded at http://mcx.space/bp.

Scalable and accessible personalized photodynamic therapy optimization with FullMonte and PDT-SPACE

SIGNIFICANCE

Open-source software packages have been extensively used in the past three decades in medical imaging and diagnostics, aiming to study the feasibility of the application ex vivo. Unfortunately, most of the existing open-source tools require some software engineering background to install the prerequisite libraries, choose a suitable computational platform, and combine several software tools to address different applications.

AIM

To facilitate the use of open-source software in medical applications, enabling computational studies of treatment outcomes prior to the complex in-vivo setting.

APPROACH

FullMonteWeb, an open-source, user-friendly web-based software with a graphical user interface for interstitial photodynamic therapy (iPDT) modeling, visualization, and optimization, is introduced. The software can perform Monte Carlo simulations of light propagation in biological tissues, along with iPDT plan optimization. FullMonteWeb installs and runs the required software and libraries on Amazon Web Services (AWS), allowing scalable computing without complex set up.

RESULTS

FullMonteWeb allows simulation of large and small problems on the most appropriate compute hardware, enabling cost improvements of 10  ×   versus always running on a single platform. Case studies in optical property estimation and diffuser placement optimization highlight FullMonteWeb's versatility.

CONCLUSION

The FullMonte open source suite enables easier and more cost-effective in-silico studies for iPDT.

Multi-scattering software part II: experimental validation for the light intensity distribution

This article, Part II of an article series on GPU-accelerated Monte Carlo simulation of photon transport through turbid media, focuses on the validation of the online software Multi-Scattering. While Part I detailed the implementation of the computational model, simulated and experimental results are now compared for the distribution of the scattered light intensity. The scattering phantoms prepared here are aqueous dispersions of polystyrene microspheres of diameter D = 0.5, 2 and 5 μm and at various concentrations, resulting in optical depth ranging from OD = 1 to 17.5. The Lorenz-Mie scattering phase functions used in the simulations have been verified experimentally at low particle concentrations by analyzing the angular light intensity distribution at the Fourier plane of a collecting lens. The validation approach herein accounts for the specific light collection and image formation by the camera. The front and side surfaces of the medium are imaged and the corresponding light intensity distributions are compared qualitatively and quantitatively. It is concluded that the model enables reliable simulations over the tested parameters, offering predictive simulations of transmitted intensities with a mean relative error ≤~19% over the full range. The online software is available at: https://multi-scattering.com/.

MCX Cloud-a modern, scalable, high-performance and in-browser Monte Carlo simulation platform with cloud computing

SIGNIFICANCE

Despite the ample progress made toward faster and more accurate Monte Carlo (MC) simulation tools over the past decade, the limited usability and accessibility of these advanced modeling tools remain key barriers to widespread use among the broad user community.

AIM

An open-source, high-performance, web-based MC simulator that builds upon modern cloud computing architectures is highly desirable to deliver state-of-the-art MC simulations and hardware acceleration to general users without the need for special hardware installation and optimization.

APPROACH

We have developed a configuration-free, in-browser 3D MC simulation platform-Monte Carlo eXtreme (MCX) Cloud-built upon an array of robust and modern technologies, including a Docker Swarm-based cloud-computing backend and a web-based graphical user interface (GUI) that supports in-browser 3D visualization, asynchronous data communication, and automatic data validation via JavaScript Object Notation (JSON) schemas.

RESULTS

The front-end of the MCX Cloud platform offers an intuitive simulation design, fast 3D data rendering, and convenient simulation sharing. The Docker Swarm container orchestration backend is highly scalable and can support high-demand GPU MC simulations using MCX over a dynamically expandable virtual cluster.

CONCLUSION

MCX Cloud makes fast, scalable, and feature-rich MC simulations readily available to all biophotonics researchers without overhead. It is fully open-source and can be freely accessed at http://mcx.space/cloud.

Modeling the efficiency of UV at 254 nm for disinfecting the different layers within N95 respirators

The study presented a Monte Carlo simulation of light transport in eight commonly used filtered facepiece respirators (FFRs) to assess the efficacy of UV at 254 nm for the inactivation of SARS-CoV-2. The results showed different fluence rates across the thickness of the eight different FFRs, implying that some FFR models may be more treatable than others, with the following order being (from most to least treatable): models 1512, 9105s, 1805, 9210, 1870+, 8210, 8110s and 1860, for single side illumination. The model predictions did not coincide well with some previously reported experimental data on virus inactivation when applied to FFR surfaces. The simulations predicted that FFRs should experience higher log reductions (>>6-log) than those observed experimentally (often limited to ~5-log). Possible explanations are virus shielding by aggregation or soiling, and a lack of the Monte Carlo simulations considering near-field scattering effects that can create small, localized regions of low UV photon probability on the surface of the fiber material. If the latter is the main cause in limiting practical UV viral decontamination, improvement might be achieved by exposing the FFR to UV isotropically from all directions, such as by varying the UV source to the FFR surface angle during treatment.

Musiré: multimodal simulation and reconstruction framework for the radiological imaging sciences

A software-based workflow is proposed for managing the execution of simulation and image reconstruction for SPECT, PET, CBCT, MRI, BLI and FMI packages in single and multimodal biomedical imaging applications. The workflow is composed of a Bash script, the purpose of which is to provide an interface to the user, and to organize data flow between dedicated programs for simulation and reconstruction. The currently incorporated simulation programs comprise GATE for Monte Carlo simulation of SPECT, PET and CBCT, SpinScenario for simulating MRI, and Lipros for Monte Carlo simulation of BLI and FMI. Currently incorporated image reconstruction programs include CASToR for SPECT and PET as well as RTK for CBCT. MetaImage (mhd) standard is used for voxelized phantom and image data format. Meshlab project (mlp) containers incorporating polygon meshes and point clouds defined by the Stanford triangle format (ply) are employed to represent anatomical structures for optical simulation, and to represent tumour cell inserts. A number of auxiliary programs have been developed for data transformation and adaptive parameter assignment. The software workflow uses fully automatic distribution to, and consolidation from, any number of Linux workstations and CPU cores. Example data are presented for clinical SPECT, PET and MRI systems using the Mida head phantom and for preclinical X-ray, PET and BLI systems employing the Digimouse phantom. The presented method unifies and simplifies multimodal simulation setup and image reconstruction management and might be of value for synergistic image research. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.

Optimizing Interstitial Photodynamic Therapy Planning With Reinforcement Learning-Based Diffuser Placement

Interstitial photodynamic therapy (iPDT) has shown promising results recently as a minimally invasive stand-alone or intra-operative cancer treatment. The development of non-toxic photosensitizing drugs with improved target selectivity has increased its efficacy. However, personalized treatment planning that determines the number of photon emitters, their positions and their input powers while taking into account tissue anatomy and treatment response is still lacking to further improve outcomes.

OBJECTIVE

To develop new algorithms that generate high-quality plans by optimizing over the light source positions, along with their powers, to minimize the damage to organs-at-risk while eradicating the tumor. The optimization algorithms should also accurately model the physics of light propagation through the use of Monte-Carlo simulators.

METHODS

We use simulated-annealing as a baseline algorithm to place the sources. We propose different source perturbations that are likely to provide better outcomes and study their impact. To minimize the number of moves attempted (and effectively runtime) without degrading result quality, we use a reinforcement learning-based method to decide which perturbation strategy to perform in each iteration. We simulate our algorithm on virtual brain tumors modeling real glioblastoma multiforme cases, assuming a 5-ALA PpIX induced photosensitizer that is activated at [Formula: see text] wavelength.

RESULTS

The algorithm generates plans that achieve an average of 46% less damage to organs-as-risk compared to the manual placement used in current clinical studies.

SIGNIFICANCE

Having a general and high-quality planning system makes iPDT more effective and applicable to a wider variety of oncological indications. This paves the way for more clinical trials.

Light transport modeling in highly complex tissues using the implicit mesh-based Monte Carlo algorithm

The mesh-based Monte Carlo (MMC) technique has grown tremendously since its initial publication nearly a decade ago. It is now recognized as one of the most accurate Monte Carlo (MC) methods, providing accurate reference solutions for the development of novel biophotonics techniques. In this work, we aim to further advance MMC to address a major challenge in biophotonics modeling, i.e. light transport within highly complex tissues, such as dense microvascular networks, porous media and multi-scale tissue structures. Although the current MMC framework is capable of simulating light propagation in such media given its generality, the run-time and memory usage grow rapidly with increasing media complexity and size. This greatly limits our capability to explore complex and multi-scale tissue structures. Here, we propose a highly efficient implicit mesh-based Monte Carlo (iMMC) method that incorporates both mesh- and shape-based tissue representations to create highly complex yet memory-efficient light transport simulations. We demonstrate that iMMC is capable of providing accurate solutions for dense vessel networks and porous tissues while reducing memory usage by greater than a hundred- or even thousand-fold. In a sample network of microvasculature, the reduced shape complexity results in nearly 3x speed acceleration. The proposed algorithm is now available in our open-source MMC software at http://mcx.space/#mmc.

Review of in vivo optical molecular imaging and sensing from x-ray excitation

SIGNIFICANCE

Deep-tissue penetration by x-rays to induce optical responses of specific molecular reporters is a new way to sense and image features of tissue function in vivo. Advances in this field are emerging, as biocompatible probes are invented along with innovations in how to optimally utilize x-ray sources.

AIM

A comprehensive review is provided of the many tools and techniques developed for x-ray-induced optical molecular sensing, covering topics ranging from foundations of x-ray fluorescence imaging and x-ray tomography to the adaptation of these methods for sensing and imaging in vivo.

APPROACH

The ways in which x-rays can interact with molecules and lead to their optical luminescence are reviewed, including temporal methods based on gated acquisition and multipoint scanning for improved lateral or axial resolution.

RESULTS

While some known probes can generate light upon x-ray scintillation, there has been an emergent recognition that excitation of molecular probes by x-ray-induced Cherenkov light is also possible. Emission of Cherenkov radiation requires a threshold energy of x-rays in the high kV or MV range, but has the advantage of being able to excite a broad range of optical molecular probes. In comparison, most scintillating agents are more readily activated by lower keV x-ray energies but are composed of crystalline inorganic constituents, although some organic biocompatible agents have been designed as well. Methods to create high-resolution structured x-ray-optical images are now available, based upon unique scanning approaches and/or a priori knowledge of the scanned x-ray beam geometry. Further improvements in spatial resolution can be achieved by careful system design and algorithm optimization. Current applications of these hybrid x-ray-optical approaches include imaging of tissue oxygenation and pH as well as of certain fluorescent proteins.

CONCLUSIONS

Discovery of x-ray-excited reporters combined with optimized x-ray scan sequences can improve imaging resolution and sensitivity.

Multi-Scattering software: part I: online accelerated Monte Carlo simulation of light transport through scattering media

In this article we present and describe an online freely accessible software called Multi-Scattering for the modeling of light propagation in scattering and absorbing media. Part II of this article series focuses on the validation of the model by rigorously comparing the simulated results with experimental data. The model is based on the use of the Monte Carlo method, where billions of photon packets are being tracked through simulated cubic volumes. Simulations are accelerated by the use of general-purpose computing on graphics processing units, reducing the computation time by a factor up to 200x in comparison with a single central processing unit thread. By using four graphic cards on a single computer, the simulation speed increases by a factor of 800x. For an anisotropy factor g = 0.86, this enables the transport path of one billion photons to be computed in 10 seconds for optical depth OD = 10 and in 20 minutes for OD = 500. Another feature of Multi-Scattering is the integration and implementation of the Lorenz-Mie theory in the software to generate the scattering phase functions from spherical particles. The simulations are run from a computer server at Lund University, allowing researchers to log in and use it freely without any prior need for programming skills or specific software/hardware installations. There are countless types of scattering media in which this model can be used to predict light transport, including medical tissues, blood samples, clouds, smoke, fog, turbid liquids, spray systems, etc. An example of simulation results is given here for photon propagation through a piece of human head. The software also includes features for modeling image formation by inserting a virtual collecting lens and a detection matrix which simulate a camera objective and a sensor array respectively. The user interface for setting-up simulations and for displaying the corresponding results is found at: https://multi-scattering.com/.

Light propagation within N95 filtered face respirators: A simulation study for UVC decontamination

This study presents numerical simulations of UVC light propagation through seven different filtered face respirators (FFR) to determine their suitability for Ultraviolet germicidal inactivation (UVGI). UV propagation was modeled using the FullMonte program for two external light illuminations. The optical properties of the dominant three layers were determined using the inverse adding doubling method. The resulting fluence rate volume histograms and the lowest fluence rate recorded in the modeled volume, sometimes in the nW cm^-2 , provide feedback on a respirator's suitability for UVGI and the required exposure time for a given light source. While UVGI can present an economical approach to extend an FFR's useable lifetime, it requires careful optimization of the illumination setup and selection of appropriate respirators.

Development of a Predictive Monte Carlo Radiative Transfer Model for Ablative Fractional Skin Lasers

It is possible to enhance topical drug delivery by pretreatment of the skin with ablative fractional lasers (AFLs). However, the parameters to use for a given AFL to achieve the desired depth of ablation or the desired therapeutic or cosmetic outcome are hard to predict. This leaves open the real possibility of overapplication or underapplication of laser energy to the skin. In this study, we developed a numerical model consisting of a Monte Carlo radiative transfer (MCRT) code coupled to a heat transfer and tissue damage algorithm. The simulation is designed to predict the depth effects of AFL on the skin, verified with in vitro experiments in porcine skin via optical coherence tomography (OCT) imaging. Ex vivo porcine skin is irradiated with increasing energies (50-400 mJ/pixel) from a CO₂ AFL. The depth of microscopic treatment zones is measured and compared with our numerical model. The data from the OCT images and MCRT model complement each other well. Nonablative thermal effects on surrounding tissue are also discussed. This model, therefore, provides an initial step toward a predictive determination of the effects of AFL on the skin. Lasers Surg. Med. © 2020 The Authors. Lasers in Surgery and Medicine published by Wiley Periodicals LLC.

Hybrid mesh and voxel based Monte Carlo algorithm for accurate and efficient photon transport modeling in complex bio-tissues

Over the past decade, an increasing body of evidence has suggested that three-dimensional (3-D) Monte Carlo (MC) light transport simulations are affected by the inherent limitations and errors of voxel-based domain boundaries. In this work, we specifically address this challenge using a hybrid MC algorithm, namely split-voxel MC or SVMC, that combines both mesh and voxel domain information to greatly improve MC simulation accuracy while remaining highly flexible and efficient in parallel hardware, such as graphics processing units (GPU). We achieve this by applying a marching-cubes algorithm to a pre-segmented domain to extract and encode sub-voxel information of curved surfaces, which is then used to inform ray-tracing computation within boundary voxels. This preservation of curved boundaries in a voxel data structure demonstrates significantly improved accuracy in several benchmarks, including a human brain atlas. The accuracy of the SVMC algorithm is comparable to that of mesh-based MC (MMC), but runs 2x-6x faster and requires only a lightweight preprocessing step. The proposed algorithm has been implemented in our open-source software and is freely available at http://mcx.space.

Modeling voxel-based Monte Carlo light transport with curved and oblique boundary surfaces

SIGNIFICANCE

Monte Carlo (MC) light transport simulations are most often performed in regularly spaced three-dimensional voxels, a type of data representation that naturally struggles to represent boundary surfaces with curvature and oblique angles. Not accounting properly for such boundaries with an index of refractivity, mismatches can lead to important inaccuracies, not only in the calculated angles of reflection and transmission but also in the amount of light that transmits through or reflects from these mismatched boundary surfaces.

AIM

A new MC light transport algorithm is introduced to deal with curvature and oblique angles of incidence when simulated photons encounter mismatched boundary surfaces.

APPROACH

The core of the proposed algorithm applies the efficient preprocessing step of calculating a gradient map of the mismatched boundaries, a smoothing step on this calculated 3D vector field to remove surface roughness due to discretization and an interpolation scheme to improve the handling of curvature.

RESULTS

Through simulations of light hitting the side of a sphere and going through a lens, the agreement of this approach with analytical solutions is shown to be strong.

CONCLUSIONS

The MC method introduced here has the advantage of requiring only slight implementation changes from the current state-of-the-art to accurately simulate mismatched boundaries and readily exploit the acceleration of general-purpose graphics processing units. A code implementation, mcxyzn, is made available and maintained at https://omlc.org/software/mc/mcxyzn/.