Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration

Deep skin homogeneity and light diffusion: An accelerated Monte Carlo model for in vivo skin characterization and consumer perception

The appearance of healthy and youthful skin is related to many factors of the skin optical properties as perceived by our visual sense. The optics of light travelling through human tissues has been extensively investigated in the field of biomedical applications, including the experimental characterization and modelling of skin optics and the propagation of light such as lasers through the layers. This work presents an innovative approach to probe deep skin by means of spectrally and spatially resolved light diffusion in the different layers of skin. Dual hyperspectral measurements of the panellist's skin are performed in vivo on subjects to obtain reflectance and light diffusion spectra. Both are simultaneously fitted by a GPU-accelerated Monte Carlo model to obtain skin optical parameters as a function of depth. The results show a clear correlation between deep skin light diffusion at wavelengths above 590 nm and the subject age, which indicates a progressive degradation of skin homogeneity with age. The effect of this orange-red light diffusion background is to alter the colour tone of the skin. A skincare product is used to show that the warmer skin colour tone is clearly perceivable to consumers when evaluating facial images with and without the product. The product effect also correlates well with hyperspectral measurements. Lastly, this innovative approach demonstrates a first step in real-time skin characterization for consumers and opens the door to customized cosmetic solutions for individual needs.

Initial non-invasive in vivo sensing of the lung using time domain diffuse optics

The in vivo diagnosis and monitoring of pulmonary disorders (caused for example by emphysema, Covid-19, immature lung tissue in infants) could be effectively supported by the non-invasive sensing of the lung through light. With this purpose, we investigated the feasibility of probing the lung by means of time-resolved diffuse optics, leveraging the increased depth (a few centimeters) attained by photons collected after prolonged propagation time (a few nanoseconds). We present an initial study that includes measurements performed on 5 healthy volunteers during a breathing protocol, using a time-resolved broadband diffuse optical spectroscopy system. Those measurements were carried out across the spectral range of 600-1100 nm at a source-detector distance of 3 cm, and at 820 nm over a longer distance (7-9 cm). The preliminary analysis of the in vivo data with a simplified homogeneous model revealed a maximum probing depth of 2.6-3.9 cm, suitable for reaching the lung. Furthermore, we observed variations in signal associated with respiration, particularly evident at long photon propagation times. However, challenges stemming from both intra- and inter-subject variability, along with inconsistencies potentially arising from conflicting scattering and absorption effects on the collected signal, hindered a clear interpretation. Aspects that require further investigation for a more comprehensive understanding are outlined.

MCOCT: an experimentally and numerically validated, open-source Monte Carlo simulator for optical coherence tomography

Here, we present MCOCT, a Monte Carlo simulator for optical coherence tomography (OCT), incorporating a Gaussian illumination scheme and bias to increase backscattered event collection. MCOCT optical fluence was numerically compared and validated to an established simulator (MCX) and showed concordance at the focus while diverging slightly with distance to it. MCOCT OCT signals were experimentally compared and validated to OCT signals acquired in tissue-mimicking phantoms with known optical properties and showed a similar attenuation pattern with increasing depth while diverging beyond 1.5 mm and proximal to layer interfaces. MCOCT may help in the design of OCT systems for a wide range of applications.

UMC-PET: a fast and flexible Monte Carlo PET simulator

Objective.The GPU-based Ultra-fast Monte Carlo positron emission tomography simulator (UMC-PET) incorporates the physics of the emission, transport and detection of radiation in PET scanners. It includes positron range, non-colinearity, scatter and attenuation, as well as detector response. The objective of this work is to present and validate UMC-PET as a a multi-purpose, accurate, fast and flexible PET simulator.Approach.We compared UMC-PET against PeneloPET, a well-validated MC PET simulator, both in preclinical and clinical scenarios. Different phantoms for scatter fraction (SF) assessment following NEMA protocols were simulated in a 6R-SuperArgus and a Biograph mMR scanner, comparing energy histograms, NEMA SF, and sensitivity for different energy windows. A comparison with real data reported in the literature on the Biograph scanner is also shown.Main results.NEMA SF and sensitivity estimated by UMC-PET where within few percent of PeneloPET predictions. The discrepancies can be attributed to small differences in the physics modeling. Running in a 11 GB GeForce RTX 2080 Ti GPU, UMC-PET is ∼1500 to ∼2000 times faster than PeneloPET executing in a single core Intel(R) Xeon(R) CPU W-2155 @ 3.30 GHz.Significance.UMC-PET employs a voxelized scheme for the scanner, patient adjacent objects (such as shieldings or the patient bed), and the activity distribution. This makes UMC-PET extremely flexible. Its high simulation speed allows applications such as MC scatter correction, faster SRM estimation for complex scanners, or even MC iterative image reconstruction.

Diffuse reflectance spectroscopy for colorectal cancer surgical guidance: towards real-time tissue characterization and new biomarkers

Colorectal cancer (CRC) is the third most common and second most deadly type of cancer worldwide, representing 11.3% of the diagnosed cancer cases and resulting in 10.2% (0.88 million) of the cancer related deaths in 2020. CRCs are typically detected at the late stage, which leads to high mortality and morbidity. Mortality and poor prognosis are partially caused by cancer recurrence and postoperative complications. Patient survival could be increased by improving precision in surgical resection using accurate surgical guidance tools based on diffuse reflectance spectroscopy (DRS). DRS enables real-time tissue identification for potential cancer margin delineation through determination of the circumferential resection margin (CRM), while also supporting non-invasive and label-free approaches for laparoscopic surgery to avoid short-term complications of open surgery as suitable. In this study, we have estimated the scattering properties and chromophore concentrations based on 2949 DRS measurements of freshly excised ex vivo specimens of 47 patients, and used this estimation to classify normal colorectal wall (CW), fat and tumor tissues. DRS measurements were performed with fiber-optic probes of 630 μm source-detector distance (SDD; probe 1) and 2500 μm SDD (probe 2) to measure tissue layers ∼0.5-1 mm and ∼0.5-2 mm deep, respectively. By using the 5-fold cross-validation of machine learning models generated with the classification and regression tree (CART) algorithm, we achieved 95.9 ± 0.7% sensitivity, 98.9 ± 0.3% specificity, 90.2 ± 0.4% accuracy, and 95.5 ± 0.3% AUC for probe 1. Similarly, we achieved 96.9 ± 0.8% sensitivity, 98.9 ± 0.2% specificity, 94.0 ± 0.4% accuracy, and 96.7 ± 0.4% AUC for probe 2.

In situ detection of human glioma based on tissue optical properties using diffuse reflectance spectroscopy

Safely maximizing brain cancer removal without injuring adjacent healthy tissue is crucial for optimal treatment outcomes. However, it is challenging to distinguish cancer from noncancer intraoperatively. This study aimed to explore the feasibility of diffuse reflectance spectroscopy (DRS) as a label-free and real-time detection technology for discrimination between brain cancer and noncancer tissues. Fifty-five fresh cancer and noncancer specimens from 19 brain surgeries were measured with DRS, and the results were compared with co-registered clinical standard histopathology. Tissue optical properties were quantitatively obtained from the diffuse reflectance spectra and compared among different types of brain tissues. A machine learning-based classifier was trained to differentiate cancerous versus noncancerous tissues. Our method could achieve a sensitivity of 93% and specificity of 95% for discriminating high-grade glioma from normal white matter. Our results showed that DRS has the potential to be used for label-free, real-time in vivo cancer detection during brain surgery.

Objective monitoring of laser tattoo removal in human volunteers using an innovative optical technique: A proof of principle

OBJECTIVES

Assess the suitability of the technique for objective monitoring of laser tattoo removal by an extended treatment protocol.

MATERIALS AND METHODS

One half of the tattoo in the first volunteer was treated with nanosecond and the other half with picosecond laser pulses at 1064 nm. In the second subject, four test areas were treated repeatedly using different radiant exposures from 1.5 to 6 J/cm2 . Measurements of diffuse reflectance spectra and photothermal radiometric transients were performed 4-20 weeks after each treatment session. Inverse Monte Carlo analysis based on a three-layer model of tattooed skin was applied to assess the tattoo characteristics and analyze their changes.

RESULTS

The results clearly indicate a gradual reduction of the ink content and an increase of the subsurface depth of the tattoo layer with all treatments at a radiant exposure of 3 J/cm2 or higher. The observed dependences on laser pulse duration, radiant exposure, and a number of treatments are in excellent agreement with visual fading of the tattoo.

CONCLUSIONS

The presented methodology enables noninvasive characterization of tattoos in human skin and objective monitoring of the laser removal treatment.

Neu(t)ralMC: energy-efficient open source Monte Carlo algorithm for assessing photon transport in turbid media

Light propagation in turbid mediums such as atmosphere, fluids, and biological tissues is a challenging problem which necessitates accurate simulation techniques to account for the effects of multiple scattering. The Monte Carlo method has long established itself as a gold standard and is widely adopted for simulating light transport, however, its computationally intensive nature often requires significant processing power and energy consumption. In this paper a novel, open source Monte Carlo algorithm is introduced which is specifically designed for use with energy-efficient processors, effectively addressing those challenges, while maintaining the accuracy/compatibility and outperforming existing solutions. The proposed implementation optimizes photon transport simulations by exploiting the unique capabilities of Apple's low-power, high-performance M-family of chips. The developed method has been implemented in an open-source software package, enabling seamless adaptation of developed algorithms for specific applications. The accuracy and performance are validated using comprehensive comparison with existing solvers commonly used for biomedical imaging. The results demonstrate that the new algorithm achieves comparable accuracy levels to those of existing techniques while significantly reducing computational time and energy consumption.

Depth Dose Enhancement in Orthovoltage Nanoparticle-Enhanced Radiotherapy: A Monte Carlo Phantom Study

BACKGROUND

This study was to examine the depth dose enhancement in orthovoltage nanoparticle-enhanced radiotherapy for skin treatment by investigating the impact of various photon beam energies, nanoparticle materials, and nanoparticle concentrations.

METHODS

A water phantom was utilized, and different nanoparticle materials (gold, platinum, iodine, silver, iron oxide) were added to determine the depth doses through Monte Carlo simulation. The clinical 105 kVp and 220 kVp photon beams were used to compute the depth doses of the phantom at different nanoparticle concentrations (ranging from 3 mg/mL to 40 mg/mL). The dose enhancement ratio (DER), which represents the ratio of the dose with nanoparticles to the dose without nanoparticles at the same depth in the phantom, was calculated to determine the dose enhancement.

RESULTS

The study found that gold nanoparticles outperformed the other nanoparticle materials, with a maximum DER value of 3.77 at a concentration of 40 mg/mL. Iron oxide nanoparticles exhibited the lowest DER value, equal to 1, when compared to other nanoparticles. Additionally, the DER value increased with higher nanoparticle concentrations and lower photon beam energy.

CONCLUSIONS

It is concluded in this study that gold nanoparticles are the most effective in enhancing the depth dose in orthovoltage nanoparticle-enhanced skin therapy. Furthermore, the results suggest that increasing nanoparticle concentration and decreasing photon beam energy lead to increased dose enhancement.

Real-time and accurate estimation ex vivo of four basic optical properties from thin tissue based on a cascade forward neural network

Double integrating sphere measurements obtained from thin ex vivo tissues provides more spectral information and hence allows full estimation of all basic optical properties (OPs) theoretically. However, the ill-conditioned nature of the OP determination increases excessively with the reduction in tissue thickness. Therefore, it is crucial to develop a model for thin ex vivo tissues that is robust to noise. Herein, we present a deep learning solution to precisely extract four basic OPs in real-time from thin ex vivo tissues, leveraging a dedicated cascade forward neural network (CFNN) for each OP with an additional introduced input of the refractive index of the cuvette holder. The results show that the CFNN-based model enables accurate and fast evaluation of OPs, as well as robustness to noise. Our proposed method overcomes the highly ill-conditioned restriction of OP evaluation and can distinguish the effects of slight changes in measurable quantities without any a priori knowledge.

Modeling Monte Carlo simulation on photon regeneration effects of perovskite FAPbI3 for photovoltaic applications

Controlling the photon regeneration effects in FAPbI₃ films is a noteworthy approach to improve the photovoltaic (PV) efficiency of FAPbI₃-based solar cells. However, the lack of systematic study on the relationship between photon regeneration effects and PV efficiency in the experimental process makes it difficult to control the photon regeneration effects effectively. In this work, we combine the Monte Carlo sampling method and the polar coordinate calculation method to design a new algorithm for a detailed simulation of the main processes of photon regeneration effects affecting the PV efficiency in a model based on an n-i-p type FAPbI₃ perovskite solar cell (PSC). The algorithm is validated to be used to compare the power-conversion efficiency (PCE) of different PSCs to filter out the PSC structure with the highest PCE or to determine the range of material parameter values corresponding to the highest PCE. This work opens up new ideas to effectively control the photon regeneration effects in PSCs to improve the device PV efficiency.

Extracting Tissue Optical Properties and Detecting Bruised Tissue in Pears Quickly and Accurately Based on Spatial Frequency Domain Imaging and Machine Learning

Recently, Spatial Frequency Domain Imaging (SFDI) has gradually become an alternative method to extract tissue optical properties (OPs), as it provides a wide-field, no-contact acquisition. SFDI extracts OPs by least-square fitting (LSF) based on the diffuse approximation equation, but there are shortcomings in the speed and accuracy of extracting OPs. This study proposed a Long Short-term Memory Regressor (LSTMR) solution to extract tissue OPs. This method allows for fast and accurate extraction of tissue OPs. Firstly, the imaging system was developed, which is more compact and portable than conventional SFDI systems. Next, numerical simulation was performed using the Monte Carlo forward model to obtain the dataset, and then the mapping model was established using the dataset. Finally, the model was applied to detect the bruised tissue of 'crown' pears. The results show that the mean absolute errors of the absorption coefficient and the reduced scattering coefficient are no more than 0.32% and 0.21%, and the bruised tissue of 'crown' pears can be highlighted by the change of OPs. Compared with the LSF, the speed of extracting tissue OPs is improved by two orders of magnitude, and the accuracy is greatly improved. The study contributes to the rapid and accurate extraction of tissue OPs based on SFDI and has great potential in food safety assessment.

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.

Wireless optoelectronic devices for vagus nerve stimulation in mice

Objective.Vagus nerve stimulation (VNS) is a promising approach for the treatment of a wide variety of debilitating conditions, including autoimmune diseases and intractable epilepsy. Much remains to be learned about the molecular mechanisms involved in vagus nerve regulation of organ function. Despite an abundance of well-characterized rodent models of common chronic diseases, currently available technologies are rarely suitable for the required long-term experiments in freely moving animals, particularly experimental mice. Due to challenging anatomical limitations, many relevant experiments require miniaturized, less invasive, and wireless devices for precise stimulation of the vagus nerve and other peripheral nerves of interest. Our objective is to outline possible solutions to this problem by using nongenetic light-based stimulation.Approach.We describe how to design and benchmark new microstimulation devices that are based on transcutaneous photovoltaic stimulation. The approach is to use wired multielectrode cuffs to test different stimulation patterns, and then build photovoltaic stimulators to generate the most optimal patterns. We validate stimulation through heart rate analysis.Main results.A range of different stimulation geometries are explored with large differences in performance. Two types of photovoltaic devices are fabricated to deliver stimulation: photocapacitors and photovoltaic flags. The former is simple and more compact, but has limited efficiency. The photovoltaic flag approach is more elaborate, but highly efficient. Both can be used for wireless actuation of the vagus nerve using light impulses.Significance.These approaches can enable studies in small animals that were previously challenging, such as long-termin vivostudies for mapping functional vagus nerve innervation. This new knowledge may have potential to support clinical translation of VNS for treatment of select inflammatory and neurologic diseases.

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.

History of Monte Carlo modeling of light transport in tissues using mcml.c

The Monte Carlo simulation called mcml.c was written and shared on-line since 1992. This perspective summarizes the contributions by the people involved in the development of mcml.c, and work by others extending the code.

Chronic electrical stimulation of peripheral nerves via deep-red light transduced by an implanted organic photocapacitor

Implantable devices for the wireless modulation of neural tissue need to be designed for reliability, safety and reduced invasiveness. Here we report chronic electrical stimulation of the sciatic nerve in rats by an implanted organic electrolytic photocapacitor that transduces deep-red light into electrical signals. The photocapacitor relies on commercially available semiconducting non-toxic pigments and is integrated in a conformable 0.1-mm³ thin-film cuff. In freely moving rats, fixation of the cuff around the sciatic nerve, 10 mm below the surface of the skin, allowed stimulation (via 50-1,000-μs pulses of deep-red light at wavelengths of 638 nm or 660 nm) of the nerve for over 100 days. The robustness, biocompatibility, low volume and high-performance characteristics of organic electrolytic photocapacitors may facilitate the wireless chronic stimulation of peripheral nerves.

Quantifying tissue optical properties of human heads in vivo using continuous-wave near-infrared spectroscopy and subject-specific three-dimensional Monte Carlo models

SIGNIFICANCE

Quantifying subject-specific optical properties (OPs) including absorption and transport scattering coefficients of tissues in the human head could improve the modeling of photon propagation for the analysis of functional near-infrared spectroscopy (fNIRS) data and dosage quantification in therapeutic applications. Current methods employ diffuse approximation, which excludes a low-scattering cerebrospinal fluid compartment and causes errors.

AIM

This work aims to quantify OPs of the scalp, skull, and gray matter in vivo based on accurate Monte Carlo (MC) modeling.

APPROACH

Iterative curve fitting was applied to quantify tissue OPs from multidistance continuous-wave NIR reflectance spectra. An artificial neural network (ANN) was trained using MC-simulated reflectance values based on subject-specific voxel-based tissue models to replace MC simulations as the forward model in curve fitting. To efficiently generate sufficient data for training the ANN, the efficiency of MC simulations was greatly improved by white MC simulations, increasing the detectors' acceptance angle, and building a lookup table for interpolation.

RESULTS

The trained ANN was six orders of magnitude faster than the original MC simulations. OPs of the three tissue compartments were quantified from NIR reflectance spectra measured at the forehead of five healthy subjects and their uncertainties were estimated.

CONCLUSIONS

This work demonstrated an MC-based iterative curve fitting method to quantify subject-specific tissue OPs in-vivo, with all OPs except for scattering coefficients of scalp within the ranges reported in the literature, which could aid the modeling of photon propagation in human heads.

A review of advances in imaging methodology in fluorescence molecular tomography

Objective. Fluorescence molecular tomography (FMT) is a promising non-invasive optical molecular imaging technology with strong specificity and sensitivity that has great potential for preclinical and clinical studies in tumor diagnosis, drug development and therapeutic evaluation. However, the strong scattering of photons and insufficient surface measurements make it very challenging to improve the quality of FMT image reconstruction and its practical application for early tumor detection. Therefore, continuous efforts have been made to explore more effective approaches or solutions in the pursuit of high-quality FMT reconstructions. Approach. This review takes a comprehensive overview of advances in imaging methodology for FMT, mainly focusing on two critical issues in FMT reconstructions: improving the accuracy of solving the forward physical model and mitigating the ill-posed nature of the inverse problem from a methodological point of view. More importantly, numerous impressive and practical strategies and methods for improving the quality of FMT reconstruction are summarized. Notably, deep learning methods are discussed in detail to illustrate their advantages in promoting the imaging performance of FMT thanks to large datasets, the emergence of optimized algorithms and the application of innovative networks. Main results. The results demonstrate that the imaging quality of FMT can be effectively promoted by improving the accuracy of optical parameter modeling, combined with prior knowledge, and reducing dimensionality. In addition, the traditional regularization-based methods and deep neural network-based methods, especially end-to-end deep networks, can enormously alleviate the ill-posedness of the inverse problem and improve the quality of FMT image reconstruction. Significance. This review aims to illustrate a variety of effective and practical methods for the reconstruction of FMT images that may benefit future research. Furthermore, it may provide some valuable research ideas and directions for FMT in the future, and could promote, to a certain extent, the development of FMT and other methods of optical tomography.

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/.

Phase function estimation from a diffuse optical image via deep learning

Objective. The phase function is a key element of a light propagation model for Monte Carlo (MC) simulation, which is usually fitted with an analytic function with associated parameters. In recent years, machine learning methods were reported to estimate the parameters of the phase function of a particular form such as the Henyey–Greenstein phase function but, to our knowledge, no studies have been performed to determine the form of the phase function. Approach. Here we design a convolutional neural network (CNN) to estimate the phase function from a diffuse optical image without any explicit assumption on the form of the phase function. Specifically, we use a Gaussian mixture model (GMM) as an example to represent the phase function generally and learn the model parameters accurately. The GMM is selected because it provides the analytic expression of phase function to facilitate deflection angle sampling in MC simulation, and does not significantly increase the number of free parameters. Main Results. Our proposed method is validated on MC-simulated reflectance images of typical biological tissues using the Henyey–Greenstein phase function with different anisotropy factors. The mean squared error of the phase function is 0.01 and the relative error of the anisotropy factor is 3.28%. Significance. We propose the first data-driven CNN-based inverse MC model to estimate the form of scattering phase function. The effects of field of view and spatial resolution are analyzed and the findings provide guidelines for optimizing the experimental protocol in practical applications.

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.

Effects of patient-specific treatment planning on eligibility for photodynamic therapy of deep tissue abscess cavities: retrospective Monte Carlo simulation study

SIGNIFICANCE

Antimicrobial photodynamic therapy (PDT) effectively kills bacterial strains found in deep tissue abscess cavities. PDT response hinges on multiple factors, including light dose, which depends on patient optical properties.

AIM

Computed tomography images for 60 abscess drainage subjects were segmented and used for Monte Carlo (MC) simulation. We evaluated effects of optical properties and abscess morphology on PDT eligibility and generated treatment plans.

APPROACH

A range of abscess wall absorptions (μa  ,  wall) and intra-cavity Intralipid concentrations were simulated. At each combination, the threshold optical power and optimal Intralipid concentration were found for a fluence rate target, with subjects being eligible for PDT if the target was attainable with <2000  mW of source light. Further simulations were performed with absorption within the cavity (μa  ,  cavity).

RESULTS

Patient-specific treatment planning substantially increased the number of subjects expected to achieve an efficacious light dose for antimicrobial PDT, especially with Intralipid modification. The threshold optical power and optimal Intralipid concentration increased with increasing μa  ,  wall (p  <  0.001). PDT eligibility improved with patient-specific treatment planning (p  <  0.0001). With μa  ,  wall  =  0.2  cm  -  1, eligibility increased from 42% to 92%. Increasing μa  ,  cavity reduced PDT eligibility (p  <  0.0001); modifying the delivered optical power had the greatest impact in this case.

CONCLUSIONS

MC-based treatment planning greatly increases eligibility for PDT of abscess cavities.

Quasiequilibrium multistate cellular automata

In our effort to tackle the problem of letting nontrivial interactions, thermodynamic equilibrium, and full synchronicity coexist, and in the hope of reviving interest in cellular automata as promising tools for the quantitative, large-scale investigation of multiparticle systems, we built a fully synchronous cellular automaton rule for the simulation of occupancy-based lattice systems with multistate cells and neighboring interactions. The core of this rule, which constitutes an actual synchronous sampling scheme, is a negotiation stage; it produces cell occupancy distributions in very good agreement with their sequential Monte Carlo counterparts, and it satisfies a cellwise detailed balance principle thanks to the use of "mixed" intermediate states that allow for the computation of locally averaged acceptance probabilities. We took a square lattice (but the rule itself is not bound by dimensionality) as a basis for comparison with sequential Monte Carlo for showing that this synchronous rule leads to quasiequilibrium; the fulfillment of cellwise detailed balance is shown through results obtained for a small one-dimensional system, where the transition matrix could be computed exactly.

Quantification of soft tissue parameters from spatially resolved diffuse reflectance finite element models

Spatially resolved diffuse reflectance spectroscopy (SRDRS) is a non-invasive optical technique that helps in clinical diagnosis of various tissue microcirculation and skin pigmentation disorders based on collected backscattered light from multi-layered tissue. The extraction of the optical properties from the reflectance spectrum using analytical solutions is laborious. Model-based light tissue interaction studies help in quantifying the optical properties. This work presents the use of finite element models of light tissue interaction for this purpose. A bilayer model mimicking human skin was considered and the diffused reflectance spectra at multiple detector points were generated using finite element modelling for varying melanin concentration, epidermal thickness, blood volume fraction, oxygen saturation and scattering components. The reflectance value based on varying optical parameters from multiple detection points lead to the generation of a look-up table (LUT), which is further used for finding the tissue parameters that contribute to the spatially resolved reflectance values. The tissue parameters estimated after inverse modelling showed a high degree of agreement with the expected tissue parameters for a test dataset different from the training dataset.

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.

Evaluation of Pseudo-Random Number Generation on GPU Cards

Monte Carlo methods rely on sequences of random numbers to obtain solutions to many problems in science and engineering. In this work, we evaluate the performance of different pseudo-random number generators (PRNGs) of the Curand library on a number of modern Nvidia GPU cards. As a numerical test, we generate pseudo-random number (PRN) sequences and obtain non-uniform distributions using the acceptance-rejection method. We consider GPU, CPU, and hybrid CPU/GPU implementations. For the GPU, we additionally consider two different implementations using the host and device application programming interfaces (API). We study how the performance depends on implementation parameters, including the number of threads per block and the number of blocks per streaming multiprocessor. To achieve the fastest performance, one has to minimize the time consumed by PRNG seed setup and state update. The duration of seed setup time increases with the number of threads, while PRNG state update decreases. Hence, the fastest performance is achieved by the optimal balance of these opposing effects.

Optical property recovery with spatially-resolved diffuse reflectance at short source-detector separations using a compact fiber-optic probe

We describe a compact fiber-optic probe (2 mm outside diameter) that utilizes spatially-resolved diffuse reflectance for tissue optical property recovery. Validation was performed in phantoms containing Intralipid 20% as scatterer, and methylene blue (MB), MnTPPS, and/or India ink as absorbers. Over a range of conditions, the reduced scattering coefficient was recovered with a root mean square error (RMSE) of 0.86-2.7 cm^-1 (average error = 3.8%). MB concentration was recovered with RMSE = 0.26-0.52 µM (average error = 15.0%), which did not vary with inclusion of MnTPPS (p=0.65). This system will be utilized to determine optical properties in human abscesses, in order to generate treatment plans for photodynamic therapy.

Effect of the presence of amniotic fluid for optical transabdominal fetal monitoring using Monte Carlo simulations

About a third of babies are delivered by Cesarean section. There has been an increase in maternal deaths during labor due to complications with subsequent births after a C-section. Therefore, there is a clinical motivation to reduce the C-section rate. Current techniques are, however, inefficient at determining fetal distress leading to a high false positive rate for complications and ultimately a C-section. For the current study, Monte Carlo simulations were used to calculate the amount of signal received on a model of a pregnant mother, as well as, the percent of the signal that comes from the fetal layer. Models with and without a 1 mm amniotic fluid were compared and showed differing trends.

In-silico investigation towards the non-invasive optical detection of blood lactate

This paper uses Monte Carlo simulations to investigate the interaction of short-wave infrared (SWIR) light with vascular tissue as a step toward the development of a non-invasive optical sensor for measuring blood lactate in humans. The primary focus of this work was to determine the optimal source-detector separation, penetration depth of light at SWIR wavelengths in tissue, and the optimal light power required for reliable detection of lactate. The investigation also focused on determining the non-linear variations in absorbance of lactate at a few select SWIR wavelengths. SWIR photons only penetrated 1.3 mm and did not travel beyond the hypodermal fat layer. The maximum output power was only 2.51% of the input power, demonstrating the need for a highly sensitive detection system. Simulations optimized a source-detector separation of 1 mm at 1684 nm for accurate measurement of lactate in blood.

A computationally efficient Monte-Carlo model for biomedical Raman spectroscopy

Monte Carlo (MC) modeling is a valuable tool to gain fundamental understanding of light-tissue interactions, provide guidance and assessment to optical instrument designs, and help analyze experimental data. It has been a major challenge to efficiently extend MC towards modeling of bulk-tissue Raman spectroscopy (RS) due to the wide spectral range, relatively sharp spectral features, and presence of background autofluorescence. Here, we report a computationally efficient MC approach for RS by adapting the massively-parallel Monte Carlo eXtreme (MCX) simulator. Simulation efficiency is achieved through "isoweight," a novel approach that combines the statistical generation of Raman scattered and Fluorescence emission with a lookup-table-based technique well-suited for parallelization. The MC model uses a graphics processor to produce dense Raman and fluorescence spectra over a range of 800 - 2000 cm^-1 with an approximately 100× increase in speed over prior RS Monte Carlo methods. The simulated RS signals are compared against experimentally collected spectra from gelatin phantoms, showing a strong correlation.

Bridging the gap between measurement-based and simulation-based metamodels for deriving bulk optical properties from spatially-resolved reflectance profiles: effect of illumination and detection geometry

Non-invasive determination of the optical properties is essential for understanding the light propagation in biological tissues and developing optical techniques for quality detection. Simulation-based models provide flexibility in designing the search space, while measurement-based models can incorporate the unknown system responses. However, the interoperability between these two types of models is typically poor. In this research, the mismatches between measurements and simulations were explored by studying the influences from light source and the incident and detection angle on the diffuse reflectance profiles. After reducing the mismatches caused by the factors mentioned above, the simulated diffuse reflectance profiles matched well with the measurements, with R2 values above 0.99. Successively, metamodels linking the optical properties with the diffuse reflectance profiles were respectively built based on the measured and simulated profiles. The prediction performance of these metamodels was comparable, both obtaining R2 values above 0.96. Proper correction for these sources of mismatches between measurements and simulations thus allows to build a simulation-based metamodel with a wide range of desired optical properties that is applicable to different measurement configurations.

Spatial-Frequency Domain Imaging: An Emerging Depth-Varying and Wide-Field Technique for Optical Property Measurement of Biological Tissues

Measurement of optical properties is critical for understanding light-tissue interaction, properly interpreting measurement data, and gaining better knowledge of tissue physicochemical properties. However, conventional optical measuring techniques are limited in point measurement, which partly hinders the applications on characterizing spatial distribution and inhomogeneity of optical properties of biological tissues. Spatial-frequency domain imaging (SFDI), as an emerging non-contact, depth-varying and wide-field optical imaging technique, is capable of measuring the optical properties in a wide field-of-view on a pixel-by-pixel basis. This review first describes the typical SFDI system and the principle for estimating optical properties using the SFDI technique. Then, the applications of SFDI in the fields of biomedicine, as well as food and agriculture, are reviewed, including burn assessment, skin tissue evaluation, tumor tissue detection, brain tissue monitoring, and quality evaluation of agro-products. Finally, a discussion on the challenges and future perspectives of SFDI for optical property estimation is presented.

Optical signatures of radiofrequency ablation in biological tissues

Accurate monitoring of treatment is crucial in minimally-invasive radiofrequency ablation in oncology and cardiovascular disease. We investigated alterations in optical properties of ex-vivo bovine tissues of the liver, heart, muscle, and brain, undergoing the treatment. Time-domain diffuse optical spectroscopy was used, which enabled us to disentangle and quantify absorption and reduced scattering spectra. In addition to the well-known global (1) decrease in absorption, and (2) increase in reduced scattering, we uncovered new features based on sensitive detection of spectral changes. These absorption spectrum features are: (3) emergence of a peak around 840 nm, (4) redshift of the 760 nm deoxyhemoglobin peak, and (5) blueshift of the 970 nm water peak. Treatment temperatures above 100 °C led to (6) increased absorption at shorter wavelengths, and (7) further decrease in reduced scattering. This optical behavior provides new insights into tissue response to thermal treatment and sets the stage for optical monitoring of radiofrequency ablation.

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/.

GPU-accelerated Monte Carlo simulation of MV-CBCT

Monte Carlo simulation (MCS) is one of the most accurate computation methods for dose calculation and image formation in radiation therapy. However, the high computational complexity and long execution time of MCS limits its broad use. In this paper, we present a novel strategy to accelerate MCS using a graphic processing unit (GPU), and we demonstrate the application in mega-voltage (MV) cone-beam computed tomography (CBCT) simulation. A new framework that generates a series of MV projections from a single simulation run is designed specifically for MV-CBCT acquisition. A Geant4-based GPU code for photon simulation is incorporated into the framework for the simulation of photon transport through a phantom volume. The FastEPID method, which accelerates the simulation of MV images, is modified and integrated into the framework. The proposed GPU-based simulation strategy was tested for its accuracy and efficiency in a Catphan 604 phantom and an anthropomorphic pelvis phantom with beam energies at 2.5 MV, 6 MV, and 6 MV FFF. In all cases, the proposed GPU-based simulation demonstrated great simulation accuracy and excellent agreement with measurement and CPU-based simulation in terms of reconstructed image qualities. The MV-CBCT simulation was accelerated by factors of roughly 900-2300 using an NVIDIA Tesla V100 GPU card against a 2.5 GHz AMD Opteron™ Processor 6380.

Space-enhanced time-domain diffuse optics for determination of tissue optical properties in two-layered structures

A novel methodology for solving the inverse problem of diffuse optics for two-layered structures is proposed to retrieve the absolute quantities of optical absorption and reduced scattering coefficients of the layers simultaneously. A liquid phantom with various optical absorption properties in the deep layer is prepared and experimentally investigated using the space-enhanced time-domain method. Monte-Carlo simulations are applied to analyze the different measurements in time domain, space domain, and by the new methodology. The deviations of retrieved values from nominal values of both layers' optical properties are simultaneously reduced to a very low extent compared to the single-domain methods. The reliability and uncertainty of the retrieval performance are also considerably improved by the new methodology. It is observed in time-domain analyses that for the deep layer the retrieval of absorption coefficient is almost not affected by the scattering properties and this kind of "deep scattering neutrality" is investigated and overcome as well.

Experimental validation of a spectroscopic Monte Carlo light transport simulation technique and Raman scattering depth sensing analysis in biological tissue

SIGNIFICANCE

Raman spectroscopy (RS) applied to surgical guidance is attracting attention among scientists in biomedical optics. Offering a computational platform for studying depth-resolved RS and probing molecular specificity of different tissue layers is of crucial importance to increase the precision of these techniques and facilitate their clinical adoption.

AIM

The aim of this work was to present a rigorous analysis of inelastic scattering depth sampling and elucidate the relationship between sensing depth of the Raman effect and optical properties of the tissue under interrogation.

APPROACH

A new Monte Carlo (MC) package was developed to simulate absorption, fluorescence, elastic, and inelastic scattering of light in tissue. The validity of the MC algorithm was demonstrated by comparison with experimental Raman spectra in phantoms of known optical properties using nylon and polydimethylsiloxane as Raman-active compounds. A series of MC simulations were performed to study the effects of optical properties on Raman sensing depth for an imaging geometry consistent with single-point detection using a handheld fiber optics probe system.

RESULTS

The MC code was used to estimate the Raman sensing depth of a handheld fiber optics system. For absorption and reduced scattering coefficients of 0.001 and 1  mm  -  1, the sensing depth varied from 105 to 225  μm for a range of Raman probabilities from 10  -  6 to 10  -  3. Further, for a realistic Raman probability of 10  -  6, the sensing depth ranged between 10 and 600  μm for the range of absorption coefficients 0.001 to 1.4  mm  -  1 and reduced scattering coefficients of 0.5 to 30  mm  -  1.

CONCLUSIONS

A spectroscopic MC light transport simulation platform was developed and validated against experimental measurements in tissue phantoms and used to predict depth sensing in tissue. It is hoped that the current package and reported results provide the research community with an effective simulating tool to improve the development of clinical applications of RS.

Exploiting scale-invariance: a top layer targeted inverse model for hyperspectral images of wounds

Detection of re-epithelialization in wound healing is important, but challenging. Hyperspectral imaging can be used for non-destructive characterization, but efficient techniques are needed to extract and interpret the information. An inverse photon transport model suitable for characterization of re-epithelialization is validated and explored in this study. It exploits scale-invariance to enable fitting of the epidermal skin layer only. Monte Carlo simulations indicate that the fitted layer transmittance and reflectance spectra are unique, and that there exists an infinite number of coupled parameter solutions. The method is used to explain the optical behavior of and detect re-epithelialization in an in vitro wound model.

A Semianalytic Monte Carlo Simulator for Spaceborne Oceanic Lidar: Framework and Preliminary Results

Spaceborne lidar (light detection and ranging) is a very promising tool for the optical properties of global atmosphere and ocean detection. Although some studies have shown spaceborne lidar’s potential in ocean application, there is no spaceborne lidar specifically designed for ocean studies at present. In order to investigate the detection mechanism of the spaceborne lidar and analyze its detection performance, a spaceborne oceanic lidar simulator is established based on the semianalytic Monte Carlo (MC) method. The basic principle, the main framework, and the preliminary results of the simulator are presented. The whole process of the laser emitting, transmitting, and receiving is executed by the simulator with specific atmosphere–ocean optical properties and lidar system parameters. It is the first spaceborne oceanic lidar simulator for both atmosphere and ocean. The abilities of this simulator to characterize the effect of multiple scattering on the lidar signals of different aerosols, clouds, and seawaters with different scattering phase functions are presented. Some of the results of this simulator are verified by the lidar equation. It is confirmed that the simulator is beneficial to study the principle of spaceborne oceanic lidar and it can help develop a high-precision retrieval algorithm for the inherent optical properties (IOPs) of seawater.

Fast and accurate Monte Carlo simulations of subdiffusive spatially resolved reflectance for a realistic optical fiber probe tip model aided by a deep neural network

In this work, we introduce a framework for efficient and accurate Monte Carlo (MC) simulations of spatially resolved reflectance (SRR) acquired by optical fiber probes that account for all the details of the probe tip including reflectivity of the stainless steel and the properties of the epoxy fill and optical fibers. While using full details of the probe tip is essential for accurate MC simulations of SRR, the break-down of the radial symmetry in the detection scheme leads to about two orders of magnitude longer simulation times. The introduced framework mitigates this performance degradation, by an efficient reflectance regression model that maps SRR obtained by fast MC simulations based on a simplified probe tip model to SRR simulated using the full details of the probe tip. We show that a small number of SRR samples is sufficient to determine the parameters of the regression model. Finally, we use the regression model to simulate SRR for a stainless steel optical probe with six linearly placed fibers and experimentally validate the framework through the use of inverse models for estimation of absorption and reduced scattering coefficients and subdiffusive scattering phase function quantifiers.

Non-invasive investigation of adipose tissue by time domain diffuse optical spectroscopy

The human abdominal region is very heterogeneous and stratified with subcutaneous adipose tissue (SAT) being one of the primary layers. Monitoring this tissue is crucial for diagnostic purposes and to estimate the effects of interventions like caloric restriction or bariatric surgery. However, the layered nature of the abdomen poses a major problem in monitoring the SAT in a non-invasive way by diffuse optics. In this work, we examine the possibility of using multi-distance broadband time domain diffuse optical spectroscopy to assess the human abdomen non-invasively. Broadband absorption and reduced scattering spectra from 600 to 1100 nm were acquired at 1, 2 and 3 cm source-detector distances on ten healthy adult male volunteers, and then analyzed using a homogeneous model as an initial step to understand the origin of the detected signal and how tissue should be modeled to derive quantitative information. The results exhibit a clear influence of the layered nature on the estimated optical properties. Clearly, the underlying muscle makes a relevant contribution in the spectra measured at the largest source-detector distance for thinner subjects related to blood and water absorption. More unexpectedly, also the thin superficial skin layer yields a direct contamination, leading to higher water content and steeper reduced scattering spectra at the shortest distance, as confirmed also by simulations. In conclusion, provided that data analysis properly accounts for the complex tissue structure, diffuse optics may offer great potential for the continuous non-invasive monitoring of abdominal fat.

Validation of irtGPUMCD, a GPU-based Monte Carlo internal dosimetry framework for radionuclide therapy

PURPOSE

Monte Carlo (MC) simulations are highly desirable for dose treatment planning and evaluation in radiation oncology. This is true also in emerging nuclear medicine applications such as internal radiotherapy with radionuclides. The purpose of this study is the validation of irtGPUMCD, a GPU-based MC code for dose calculations in internal radiotherapy.

METHODS

The female and male phantoms of the International Commission on Radiological Protection (ICRP 110) were used as benchmarking geometries for this study focused on ¹⁷⁷Lu and including ^99mTc and ¹³¹I. Dose calculations were also conducted for a real patient. For phantoms, twelve anatomical structures were considered as target/source organs. The S-values were evaluated with irtGPUMCD simulations (10⁸ photons), with gamma branching ratios of ICRP 107 publication. The ¹⁷⁷Lu electrons S-values were calculated for source organs only, based on local deposition of dose in irtGPUMCD. The S-value relative difference between irtGPUMCD and IDAC-DOSE were evaluated for all targets/sources considered. A DVHs comparison with GATE was conducted. An exponential track length estimator was introduced in irtGPUMCD to increase computational efficiency.

RESULTS

The relative S-value differences between irtGPUMCD and IDAC-DOSE were <5% while this comparison with GATE was <1%. The DVHs dosimetric indices comparison between GATE and irtGPUMCD for the patient led to an excellent agreement (<2%). The time required for the simulation of 10⁸ photons was 1.5 min for the female phantom, and one minute for the real patient (<1% uncertainty). These results are promising and let envision the use of irtGPUMCD for internal dosimetry in clinical applications.

Cranial Perforation Using an Optically-Enhanced Surgical Drill

The design of mechanically clutched cranial perforators, used in craniotomy procedures, limits their performance under certain clinical conditions and can, in some cases, impose the risk of severe brain injury on patients undergoing the procedure. An additional safety mechanism could help in mitigating these risks. In this work, we examine the use of diffuse reflectance spectroscopy as a potential fallback mechanism for near real-time detection of the bone-brain boundary. Monte Carlo simulation of a two layer model with optical properties of bone and brain at 530 and 850 nm resulted in a detectable change in diffuse reflectance signal when approaching the boundary. The simulated results were used to guide the development of an experimental drill control system, which was tested on 10 sheep craniums and yielded 88.1 % success rate in the detection of the approaching bone-brain boundary.

Photodynamic therapy dosimetry using multiexcitation multiemission wavelength: toward real-time prediction of treatment outcome

Evaluating the optical properties of biological tissues is needed to achieve accurate dosimetry during photodynamic therapy (PDT). Currently, accurate assessment of the photosensitizer (PS) concentration by fluorescence measurements during PDT is typically hindered by the lack of information about tissue optical properties. In the present work, a hand-held fiber-optic probe instrument monitoring fluorescence and reflectance is used for assessing blood volume, reduced scattering coefficient, and PS concentration facilitating accurate dosimetry for PDT. System validation was carried out on tissue phantoms using nonlinear least squares support machine regression analysis. It showed a high correlation coefficient (>0.99) in the prediction of the PS concentration upon a large variety of phantom optical properties. In vivo measurements were conducted in a PDT chlorine e6 dose escalating trial involving 36 male Swiss mice with Ehrlich solid tumors in which fluences of 5, 15, and 40  J cm  -  2 were delivered at two fluence rates (100 and 40  mW cm  -  2). Remarkably, quantitative measurement of fluorophore concentration was achieved in the in vivo experiment. Diffuse reflectance spectroscopy (DRS) system was also used to independently measure the physiological properties of the target tissues for result comparisons. Then, blood volume and scattering coefficient measured by the fiber-optic probe system were compared with the corresponding result measured by DRS and showed agreement. Additionally, tumor hemoglobin oxygen saturation was measured using the DRS system. Overall, the system is capable of assessing the implicit photodynamic dose to predict the PDT outcome.

Hyperspectral evaluation of peritoneal fibrosis in mouse models

Analysis of morphological changes of the peritoneal membrane is an essential part of animal studies when investigating molecular mechanisms involved in the development of peritoneal fibrosis or testing the effects of potential therapeutic agents. Current methods, such as histology and immunohistochemistry, require time consuming sample processing and analysis and result in limited spatial information. In this paper we present a new method to evaluate structural and chemical changes in an animal model of peritoneal fibrosis that is based on hyperspectral imaging and a model of light transport. The method is able to distinguish between healthy and diseased subjects based on morphological as well as physiological parameters such as blood and scattering parameters. Furthermore, it enables evaluation of changes, such as degree of inflammation and fibrosis, that are closely related to histological findings.