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The use of iodine-131 S values based on reference computational phantoms with fixed thyroid model may lead to significant dosimetric errors in patients who may have different thyroid location from the reference phantoms. In the present study, we investigated individual thyroid location variation by examining the computed tomography image sets of 40 adult male and female patients. Subsequently, the thyroid location of the adult male and female mesh-type reference phantoms of the International Commission on Radiological Protection (ICRP) was adjusted to match each the highest, mean and the lowest locations of the thyroid observed in this dataset. The thyroid-adjusted phantoms were implemented into the Geant4 Monte Carlo code to calculate thyroid location-dependent iodine-131 S values (rT ← thyroid) for a total of 30 target regions. The maximum variation among the observed thyroid locations was 39 mm and 36 mm for male and female patients, respectively. The mean thyroid locations of both male and female patients showed a good agreement with the ICRP reference phantoms. The thyroid location-dependent Iodine-131 S values were significantly different from the reference phantoms for most target regions by up to a factor of 3. The use of thyroid location-dependent S values in dose reconstructions should help quantify the dosimetric uncertainty in epidemiologic investigations of patients receiving iodine-131 therapy for hyperthyroidism and thyroid cancer.

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.

This work was supported by the EPSRC (Grant No. EP/K503162/1). Significance : Optical microscopy is characterized by the ability to get high resolution, below 1 μm, high contrast, functional and quantitative images. The use of shaped illumination, such as with lightsheet microscopy, has led to greater three-dimensional isotropic resolution with low phototoxicity. However, in most complex samples and tissues, optical imaging is limited by scattering. Many solutions to this issue have been proposed, from using passive approaches such as Bessel beam illumination to active methods incorporating aberration correction, but making fair comparisons between different approaches has proven to be challenging. Aim : We present a phase-encoded Monte Carlo radiation transfer algorithm (φMC) capable of comparing the merits of different illumination strategies or predicting the performance of an individual approach. Approach : We show that φMC is capable of modeling interference phenomena such as Gaussian or Bessel beams and compare the model with experiment. Results : Using this verified model, we show that, for a sample with homogeneously distributed scatterers, there is no inherent advantage to illuminating a sample with a conical wave (Bessel beam) instead of a spherical wave (Gaussian beam), except for maintaining a greater depth of focus. Conclusion : φMC is adaptable to any illumination geometry, sample property, or beam type (such as fractal or layered scatterer distribution) and as such provides a powerful predictive tool for optical imaging in thick samples. Publisher PDF

Significance: Diffuse light is ubiquitous in biomedical optics and imaging. Understanding the process of migration of an initial photon population entering tissue to a completely randomized, diffusely scattered population provides valuable insight to the interpretation and design of optical measurements. Aim: The goal of this perspective is to present a brief, unifying analytical framework to describe how properties of light transition from an initial state to a distributed state as light diffusion occurs. Approach: First, measurement parameters of light are introduced, and Monte Carlo simulations along with a simple analytical expression are used to explore how these individual parameters might exhibit diffusive behavior. Second, techniques to perform optical measurements are considered, highlighting how various measurement parameters can be leveraged to subsample photon populations. Results: Simulation results reinforce the fact that light undergoes a transition from a non-diffuse population to one that is first subdiffuse and then fully diffuse. Myriad experimental methods exist to isolate subpopulations of photons, which can be broadly categorized as source- and/or detector-encoded techniques, as well as methods of tagging the tissue of interest. Conclusions: Characteristic properties of light progressing to diffusion can be described by some form of Gaussian distribution that grows in space, time, angle, wavelength, polarization, and coherence. In some cases, these features can be approximated by simpler exponential behavior. Experimental methods to subsample features of the photon distribution can be achieved or theoretical methods can be used to better interpret the data with this framework.

Proton imaging is a promising technology for proton radiotherapy as it can be used for: (1) direct sampling of the tissue stopping power, (2) input information for multi-modality RSP reconstruction, (3) gold-standard calibration against concurrent techniques, (4) tracking motion and (5) pre-treatment positioning. However, no end-to-end characterization of the image quality (signal-to-noise ratio and spatial resolution, blurring uncertainty) against the dose has been done. This work aims to establish a model relating these characteristics and to describe their relationship with proton energy and object size. The imaging noise originates from two processes: the Coulomb scattering with the nucleus, producing a path deviation, and the energy loss straggling with electrons. The noise is found to increases with thickness crossed and, independently, decreases with decreasing energy. The scattering noise is dominant around high-gradient edge whereas the straggling noise is maximal in homogeneous regions. Image quality metrics are found to behave oppositely against energy: lower energy minimizes both the noise and the spatial resolution, with the optimal energy choice depending on the application and location in the imaged object. In conclusion, the model presented will help define an optimal usage of proton imaging to reach the promised application of this technology and establish a fair comparison with other imaging techniques.

Over the past decades, the application of single-photon emission computed tomography and positron emission tomography in neuroimaging has markedly increased. In the current study, we used a series of Korean computational head phantoms with detailed cranial structures for 6-, 9-, 12-, 15-y-old children and adult and a Monte Carlo transport code, MCNPX, to calculate age-dependent specific absorbed fraction (SAF) for mono-energetic electrons ranging from 0.01 to 4 MeV and S values for seven radionuclides widely used in nuclear medicine neuroimaging for the combination of ten source and target regions. Compared to the adult phantom, the 6-y phantom showed up to 1.7-fold greater SAF (cerebellum < cerebellum) and up to 1.4-fold greater S values (vitreous body < lens) for 123I. The electron SAF data, combined with our previous photon SAF data, will facilitate absorbed dose calculations for various cranial structures in patients undergoing neuroimaging procedures.

Purpose. To develop a framework to include oxygenation effects in radiation therapy treatment planning which is valid for all modalities, energy spectra and oxygen levels. The framework is based on predicting the difference in DNA-damage resulting from ionising radiation at variable oxygenation levels. Methods. Oxygen fixation is treated as a statistical process in a simplified model of complex and simple damage. We show that a linear transformation of the microscopic oxygen fixation process allows to extend this to all energies and modalities, resulting in a relatively simple rational polynomial expression. The model is expanded such that it can be applied for polyenergetic beams. The methodology is validated using Microdosimetric Monte Carlo Damage Simulation code (MCDS). This serves as a bootstrap to determine relevant parameters in the analytical expression, as MCDS is shown to be extensively verified with published empirical data. Double-strand break induction as calculated by this methodology is compared to published proton experiments. Finally, an example is worked out where the oxygen enhancement ratio (OER) is calculated at different positions in a clinically relevant spread out Bragg peak (SOBP) dose deposition in water. This dose deposition is obtained using a general Monte Carlo code (FLUKA) to determine dose deposition and locate fluence spectra. Results. For all modalities (electrons, protons), the damage categorised as complex could be parameterised to within 0.3% of the value calculated using microdosimetric Monte Carlo. The proton beam implementation showed some variation in OERs which differed slightly depending on where the assessment was made; before the SOBP, mid-SOBP or at the distal edge. Environment oxygenation was seen to be the more important variable. Conclusions. An analytic expression calculating complex damage depending on modality, energy spectrum, and oxygenation levels was shown to be effective and can be readily incorporated in treatment planning software, to take into account the impact of variable oxygenation, forming a first step to an optimised treatment based on biological factors.

Significance: Recovering accurate oxygenation estimations in the breast with quantitative photoacoustic tomography (QPAT) is not straightforward. Accurate light fluence models are required, but the unknown ground truth of the breast makes it difficult to validate them. Phantoms are often used for the validation, but most reported phantoms have a simple architecture. Fluence models developed in these simplistic objects are not accurate for application on the complex tissues of the breast. Aim: We present a sophisticated breast phantom platform for photoacoustic (PA) and ultrasound (US) imaging in general, and specifically for QPAT. The breast phantom is semi-anthropomorphic in distribution of optical and acoustic properties and contains wall-less channels with blood. Approach: 3D printing approaches are used to develop the solid 3D breast phantom from custom polyvinyl chloride plastisol formulations and additives for replicating the tissue optical and acoustic properties. A flow circuit was developed to flush the channels with bovine blood with a controlled oxygen saturation level. To showcase the phantom’s functionality, PA measurements were performed on the phantom with two oxygenation levels. Image reconstructions with and without fluence compensation from Monte Carlo simulations were analyzed for the accuracy of oxygen saturation estimations. Results: We present design aspects of the phantom, demonstrate how it is developed, and present its breast-like appearance in PA and US imaging. The oxygen saturations were estimated in two regions of interest with and without using the fluence models. The fluence compensation positively influenced the SO2 estimations in all cases and confirmed that highly accurate fluence models are required to minimize estimation errors. Conclusions: This phantom allows studies to be performed in PA in carefully controlled laboratory settings to validate approaches to recover both qualitative and quantitative features sought after in in-vivo studies. We believe that testing with phantoms of this complexity can streamline the transition of new PA technologies from the laboratory to studies in the clinic.

Significance: Time domain diffuse correlation spectroscopy (TD-DCS) can offer increased sensitivity to cerebral hemodynamics and reduced contamination from extracerebral layers by differentiating photons based on their travel time in tissue. We have developed rigorous simulation and evaluation procedures to determine the optimal time gate parameters for monitoring cerebral perfusion considering instrumentation characteristics and realistic measurement noise. Aim: We simulate TD-DCS cerebral perfusion monitoring performance for different instrument response functions (IRFs) in the presence of realistic experimental noise and evaluate metrics of sensitivity to brain blood flow, signal-to-noise ratio (SNR), and ability to reject the influence of extracerebral blood flow across a variety of time gates to determine optimal operating parameters. Approach: Light propagation was modeled on an MRI-derived human head geometry using Monte Carlo simulations for 765- and 1064-nm excitation wavelengths. We use a virtual probe with a source–detector separation of 1 cm placed in the pre-frontal region. Performance metrics described above were evaluated to determine optimal time gate(s) for different IRFs. Validation of simulation noise estimates was done with experiments conducted on an intralipid-based liquid phantom. Results: We find that TD-DCS performance strongly depends on the system IRF. Among Gaussian pulse shapes, ∼300 ps pulse length appears to offer the best performance, at wide gates (500 ps and larger) with start times 400 and 600 ps after the peak of the TPSF at 765 and 1064 nm, respectively, for a 1-s integration time at photon detection rates seen experimentally (600 kcps at 765 nm and 4 Mcps at 1064 nm). Conclusions: Our work shows that optimal time gates satisfy competing requirements for sufficient sensitivity and sufficient SNR. The achievable performance is further impacted by system IRF with ∼300 ps quasi-Gaussian pulse obtained using electro-optic laser shaping providing the best results.

Significance: Physiological parameters extracted from diffuse reflectance spectroscopy (DRS) provide clinicians quantitative information about tissue that helps aid in diagnosis. There is a great need for an accurate and cost-effective method for extracting parameters from DRS measurements. Aim: The aim is to explore the accuracy and speed of physiological parameter extraction using machine learning models compared to that of the widely used Monte Carlo lookup table (MCLUT) inverse model. Approach: Diffuse reflectance spectra were simulated using a light transport model based on Monte Carlo simulations and weighted to six wavelengths. Deep learning (DL), random forest (RF), gradient boosting machine (GBM), and generalized linear model (GLM) machine learning models were built using a training set of 10,000 spectra from the simulated data. The MCLUT and machine learning models were used to predict physiological parameters from a separate test set of 30,000 simulated spectra. Mean absolute errors were calculated to evaluate the accuracy and compare it among MCLUT and machine learning models. In addition, the computational time to predict parameters from the test set was recorded to compare the speed among MCLUT and machine learning models. Results: The DL, RF, GBM, and GLM models all had significantly lower errors than the MCLUT inverse method for six wavelengths. The DL model proved to have the lowest errors, with all absolute percent errors under 10%. The DL model had much faster runtimes than the MCLUT. Conclusions: Machine learning is promising for extracting physiological parameters from six-wavelength DRS data, with both lower errors and a faster runtime than the widely used MCLUT model.

Reflectance spectroscopy techniques are used to determine optical properties of tissue and relate these to various types of disease. In single-fiber reflectance (SFR) spectroscopy light is emitted and collected through the same fiber, connected to a broadband light source and a spectrograph to detect the reflectance versus wavelength. Compared to diffuse reflectance techniques, the sampling depth of SFR spectroscopy is smaller, in the order of a few hundred micrometers.1 This small sampling depth makes SFR spectroscopy suitable to detect small-scale, superficial changes, such as changes related to early-stage or epithelial cancers. Since SFR fibers generally have a diameter of a few hundred micrometers, they can be introduced through endoscopes or biopsy needles, which enables the use of SFR spectroscopy in internal organs such as the colon or esophagus. SFR spectroscopy has mainly been studied for medical applications in the field of oncology,2–9 but also in saturation monitoring,10 orthopedics,11,12 and to examine melanin in the skin.13 SFR measurements cannot be described by diffusion theory alone. The diffusion approximation can accurately describe the reflectance for distances between photon emission and collection of more than several transport mean free paths (1/μs′). For smaller source–detector separations, measurements are in the subdiffuse regime, where the measured reflectance is sensitive to the tissue phase function [the probability distribution of scattering angles, p(θ)].14–16 Until recently, only the model of Kanick et al.1,17 was available to extract optical properties from SFR measurements. The validity of their model was limited to tissues with modified Henyey–Greenstein (MHG) phase functions. However, for many tissue types, it has been shown that the MHG phase function underestimates scattering in the backward direction.18–24 Therefore, we recently developed a model that is valid for the wide range of phase functions that can be encountered in tissue.25–27 In this model, the reflectance is a function of the fiber diameter d, the reduced scattering coefficient μs′, the absorption coefficient μa, and the phase function parameter psb. Our model was validated with Monte Carlo (MC) simulations. Here, we validate our model experimentally on phantoms with varying concentrations of Intralipid-20% and Evans Blue by fitting our model to the measured spectra. For robust and accurate fit results, it is important that the model is not overfitting the data—a fit model should consist of the minimum number of parameters required to describe the data. To minimize the number of fit parameters, the reduced scattering coefficient is generally parameterized as μs′=a·(λ/λ0)−b, where a is the scattering amplitude, b is the scattering slope, and λ0 is a reference wavelength, and the absorption coefficient is parameterized as the sum of absorption spectra [μa(λ)] of different absorbers present in the tissue and the volume fraction (φi) of these absorbers. However, the wavelength-dependence of psb is currently not known and, therefore, a separate fit parameter for psb has to be used for each wavelength, resulting in a few hundred fit parameters. A fit of our model to a spectrum measured by a single fiber would provide a non-unique solution because the number of fit parameters would be larger than the number of data points since a separate value of psb is estimated for each wavelength and the reflectance also depends on the reduced scattering and absorption coefficient. A solution to reduce the number of fit parameters compared to the number of data points is performing measurements with two different fiber diameters—also called multi-diameter SFR (MDSFR).28 In MDSFR, a fit is performed on the spectra of both fibers simultaneously, assuming they interrogate a sample volume with the same optical properties. Thus in MDSFR, the number of fit parameters stays the same as in SFR, but the number of data points is doubled. Even so, without a parametrization of psb we are most likely still overfitting our data. To prevent overfitting, we need to parametrize the wavelength-dependence of psb by the smallest number of parameters possible—decreasing the number of fit parameters from a few hundred to just a few. A parametrization of psb might also enable measurements with only a single fiber. Previously, we validated our model based on MC simulations. In this paper, we validate our model experimentally on phantoms. Furthermore, we propose a parametrization of psb and compare fit results for our model with and without the use of this parametrization of psb. Finally, we investigate whether the psb parametrization could enable measurements with only a single fiber.

When relativistic electrons are used to irradiate tissues, such as during FLASH pre-clinical irradiations, the electron beam energy is one of the critical parameters that determine the dose distribution. Moreover, during such irradiations, linear accelerators (linacs) usually operate with significant beam loading, where a small change in the accelerator output current can lead to beam energy reduction. Optimisation of the tuning of the accelerator’s radio frequency system is often required. We describe here a robust, easy-to-use device for non-interceptive monitoring of potential variations in the electron beam energy during every linac macro-pulse of an irradiation run. Our approach monitors the accelerated electron fringe beam using two unbiased aluminium annular charge collection plates, positioned in the beam path and with apertures (5 cm in diameter) for the central beam. These plates are complemented by two thin annular screening plates to eliminate crosstalk and equalise the capacitances of the charge collection plates. The ratio of the charge picked up on the downstream collection plate to the sum of charges picked up on the both plates is sensitive to the beam energy and to changes in the energy spectrum shape. The energy sensitivity range is optimised to the investigated beam by the choice of thickness of the first plate. We present simulation and measurement data using electrons generated by a nominal 6 MeV energy linac as well as information on the design, the practical implementation and the use of this monitor.

Exposure levels to staff in interventional radiology (IR) may be significant and appropriate assessment of radiation doses is needed. Issues regarding measurements using physical dosemeters in the clinical environment still exist. The objective of this work was to explore the prerequisites for assessing staff radiation dose, based on simulations only. Personal dose equivalent, Hp(10), was assessed using simulations based on Monte Carlo methods. The position of the operator was defined using a 3D motion tracking system. X-ray system exposure parameters were extracted from the x-ray equipment. The methodology was investigated and the simulations compared to measurements during IR procedures. The results indicate that the differences between simulated and measured staff radiation doses, in terms of the personal dose equivalent quantity Hp(10), are in the order of 30–70 %. The results are promising but some issues remain to be solved, e.g. an automated tracking of movable parts such as the ceiling-mounted protection shield.

Significance: Contamination of diffuse correlation spectroscopy (DCS) measurements of cerebral blood flow (CBF) due to systemic physiology remains a significant challenge in the clinical translation of DCS for neuromonitoring. Tunable, multi-layer Monte Carlo-based (MC) light transport models have the potential to remove extracerebral flow cross-talk in cerebral blood flow index (CBFi) estimates. Aim: We explore the effectiveness of MC DCS models in recovering accurate CBFi changes in the presence of strong systemic physiology variations during a hypercapnia maneuver. Approach: Multi-layer slab and head-like realistic (curved) geometries were used to run MC simulations of photon propagation through the head. The simulation data were post-processed into models with variable extracerebral thicknesses and used to fit DCS multi-distance intensity autocorrelation measurements to estimate CBFi timecourses. The results of the MC CBFi values from a set of human subject hypercapnia sessions were compared with CBFi values estimated using a semi-infinite analytical model, as commonly used in the field. Results: Group averages indicate a gradual systemic increase in blood flow following a different temporal profile versus the expected rapid CBF response. Optimized MC models, guided by several intrinsic criteria and a pressure modulation maneuver, were able to more effectively separate CBFi changes from scalp blood flow influence than the analytical fitting, which assumed a homogeneous medium. Three-layer models performed better than two-layer ones; slab and curved models achieved largely similar results, though curved geometries were closer to physiological layer thicknesses. Conclusion: Three-layer, adjustable MC models can be useful in separating distinct changes in scalp and brain blood flow. Pressure modulation, along with reasonable estimates of physiological parameters, can help direct the choice of appropriate layer thicknesses in MC models.

Cherenkov-excited luminescence has previously been demonstrated as a method to improve the depth sensitivity of in vivo optical imaging1–4 and could be an alternative to optical imaging with fluorescence in deeper penetrance. An example application of Cherenkov-excited luminescence is to excite an oxygen-sensitive luminescent compound during radiation therapy utilizing only the radiation as an excitation source and a sensitive camera for detection, as shown in Fig. 1(a). In conventional in vivo fluorescence imaging, utilizing an excitation laser or LED light, there is an exponential decay of the source as it propagates into tissue dictated approximately by the effective attenuation coefficient, μeff, defined by diffusion theory as μeff=3μa(μa+μs′) where the latter coefficients are for absorption and transport reduced scattering, respectively.6 In Cherenkov excitation, the excitation light is produced throughout the volume directly proportional to the dose of the radiation beam for electrons above the 220 keV threshold, following the same build up and fall off with depth, as shown in Fig. 1(c). While in both cases, laser or Cherenkov excitation, the light still has to escape the tissue and is therefore attenuated exponentially by μeff on the way out at the emission wavelength bands, there is still a major benefit from having the exciting light within the volume of tissue. Yet, in comparing optical excitation to radiation beam excitation, it is hard to clearly quantify the benefits for Cherenkov, and so in this study, the (i) spatial resolution, (ii) depth sensitivity, and (iii) optimal fluorophores for Cherenkov excitation, are each examined computationally with Monte Carlo simulations. Open in a separate window Fig. 1 Schematic illustration (a) of an in vivo application of Cherenkov-excited luminescence where Cherenkov light is generated at depths into tissue. In this illustration, a mouse with a hypoxic flank tumor and normal muscle tissue are injected with an oxygen-sensitive phosphorescent compound. As the x-ray beam passes through the tissue, Cherenkov emissions occur and excite the luminescent compound. In vivo imaging can resolve the depth-integrated voxels, and the resulting estimates can be tabulated into a histogram to describe the heterogeneous extracellular oxygen concentration.5 The photon energy distribution used in subsequent Monte Carlo simulations is shown in (b) which determine the characteristics of the Cherenkov intensity and depth within the tissue. The percentage depth-dose (PDD) curves for 10 cm×10 cm photon (6 and 18 MV) and electron (6 and 18 MeV) beams in water are shown in (c) where electrons have a higher chance of interaction and deposit dose more superficially, whereas photon beams must first generate a high-energy electron through Compton scattering before Cherenkov emissions can occur. Cherenkov emissions are correlated with dose, so the PDD can be used as an estimate of the depth distribution of the optical emissions.

The purpose was to evaluate the spatial resolution in 111In-octreotide single-photom emission computed tomography (SPECT)/computed tomography (CT) imaging following reconstructions with three different ordered subset expectation maximizations (OSEM) reconstruction algorithms; attenuation corrected (AC) OSEM, AC OSEM with resolution recovery (ACRR OSEM) and Monte Carlo-based OSEM reconstruction (MC OSEM). SPECT/CT imaging of a triple line phantom containing 111In in air and water was performed. The spatial resolution, represented by the full width at half maximum (FWHM) of a line profile, was determined for each line, for X and Y direction and for all reconstructions. The mean FWHM was 12.2 mm (±standard deviation [SD] 3.7 mm) for AC OSEM, 9.3 mm (±SD 2.5 mm) for ACRR OSEM and 8.2 mm (±SD 2.0 mm) for MC OSEM. MC-based SPECT/CT reconstruction clearly improves the spatial resolution in 111In-octreotide imaging and since MC simulations can be performed for all photon energies MC OSEM has the potential to improve SPECT/CT imaging overall.

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.

Significance: We present a Monte Carlo (MC) computational framework that simulates near-infrared (NIR) hyperspectral imaging (HSI) aimed at assisting quantification of the in vivo hemodynamic and metabolic states of the exposed cerebral cortex in small animal experiments. This can be done by targeting the NIR spectral signatures of oxygenated (HbO2) and deoxygenated (HHb) hemoglobin for hemodynamics as well as the oxidative state of cytochrome-c-oxidase (oxCCO) for measuring tissue metabolism. Aim: The aim of this work is to investigate the performances of HSI for this specific application as well as to assess key factors for the future design and operation of a benchtop system. Approach: The MC framework, based on Mesh-based Monte Carlo (MMC), reproduces a section of the exposed cortex of a mouse from an in vivo image and replicates hyperspectral illumination and detection at multiple NIR wavelengths (up to 121). Results: The results demonstrate: (1) the fitness of the MC framework to correctly simulate hyperspectral data acquisition; (2) the capability of HSI to reconstruct spatial changes in the concentrations of HbO2, HHb, and oxCCO during a simulated hypoxic condition; (3) that eight optimally selected wavelengths between 780 and 900 nm provide minimal differences in the accuracy of the hyperspectral results, compared to the “gold standard” of 121 wavelengths; and (4) the possibility to mitigate partial pathlength effects in the reconstructed data and to enhance quantification of the hemodynamic and metabolic responses. Conclusions: The MC framework is proved to be a flexible and useful tool for simulating HSI also for different applications and targets.

SIGNIFICANCE: Colorectal cancer is one of the major causes of cancer-related deaths worldwide. Surgical removal of the cancerous growth is the primary treatment for this disease. A colorectal cancer surgery, however, is often unsuccessful due to the anastomotic failure that may occur following the surgical incision. Prevention of an anastomotic failure requires continuous monitoring of intestinal tissue viability during and after colorectal surgery. To date, no clinical technology exists for the dynamic and continuous monitoring of the intestinal perfusion.\ud \ud AIM: A dual-wavelength indwelling bowel photoplethysmography (PPG) sensor for the continuous monitoring of intestinal viability was proposed and characterized through a set of in silico and in vivo investigations.\ud \ud APPROACH: The in silico investigation was based on a Monte Carlo model that was executed to quantify the variables such as penetration depth and detected intensity with respect to the sensor-tissue separations and tissue perfusion. Utilizing the simulated information, an indwelling reflectance PPG sensor was designed and tested on 20 healthy volunteers. Two sets of in vivo studies were performed using the driving current intensities 20 and 40 mA for a comparative analysis, using buccal tissue as a proxy tissue-site.\ud \ud RESULTS: Both simulated and experimental results showed the efficacy of the sensor to acquire good signals through the "contact" to a "noncontact" separation of 5 mm. A very slow wavelength-dependent variation was shown in the detected intensity at the normal and hypoxic states of the tissue, whereas a decay in the intensity was found with the increasing submucosal-blood volume. The simulated detected-to-incident-photon-ratio and the experimental signal-to-noise ratio exhibited strong positive correlations, with the Pearson product-moment correlation coefficient R ranging between 0.65 and 0.87.\ud \ud CONCLUSIONS: The detailed feasibility analysis presented will lead to clinical trials utilizing the proposed sensor.

Significance: Photodynamic therapy (PDT) could become a treatment option for nonmuscle invasive bladder cancer when the current high morbidity rate associated with red light PDT and variable PDT dose can be overcome through a combination of intravesical instillation of the photosensitizer and the use of green light creating a steep PDT dose gradient. Aim: To determine how a high PDT selectivity can be maintained throughout the bladder wall considering other efficacy determining parameters, in particular, the average optical properties of the mucosal layer governing the fluence rate multiplication factor, as well as the bladder shape and the position of the emitter in relationship to the bladder wall. Approach: We present three irradiance monitoring systems and evaluate their ability to enable selective bladder PDT considering previously determined photodynamic threshold values for the bladder cancer, mucosa and urothelium in a preclinical model, and the photosensitizer’s specific uptake ratio. Monte Carlo-based light propagation simulations performed for six human bladders at the time of therapy for a range of tissue optical properties. The performance of one irradiance sensing device in a clinical phase 1B trial is presented to underline the impact of irradiance monitoring, and it is compared to the Monte Carlo-derived dose surface histogram. Results: Monte Carlo simulations showed that irradiance monitoring systems need to comprise at least three sensors. Light scattering inside the bladder void needs to be minimized to prevent increased heterogeneity of the irradiance. The dose surface histograms vary significantly depending on the bladder shape and bladder volume but are less dependent on tissue optical properties. Conclusions: We demonstrate the need for adequate irradiance monitoring independent of a photosensitizer’s specific uptake ratio.

Significance : Definitive diagnostics of many diseases is based on the histological analysis of thin tissue cuts with optical white light microscopy. Extra information on tissue structural properties obtained with polarized light would help the pathologist to improve the accuracy of his diagnosis. Aim: We report on using Mueller matrix microscopy data, logarithmic decomposition, and polarized Monte Carlo (MC) modeling for qualitative and quantitative analysis of thin tissue cuts to extract the information on tissue microstructure that is not available with a conventional white light microscopy. Approach: Unstained cuts of human skin equivalents were measured with a custom-built liquid-crystal-based Mueller microscope in transmission configuration. To interpret experimental data, we performed the simulations with a polarized MC algorithm for scattering anisotropic media. Several optical models of tissue (spherical scatterers within birefringent host medium, and combination of spherical and cylindrical scatterers within either isotropic or birefringent host medium) were tested. Results: A set of rotation invariants for the logarithmic decomposition of a Mueller matrix was derived to rule out the impact of sample orientation. These invariants were calculated for both simulated and measured Mueller matrices of the dermal layer of skin equivalents. We demonstrated that only the simulations with a model combining both spherical and cylindrical scatterers within birefringent host medium reproduced the experimental trends in optical properties of the dermal layer (linear retardance, linear dichroism, and anisotropic linear depolarization) with layer thickness. Conclusions: Our studies prove that Mueller polarimetry provides relevant information not only on a size of dominant scatterers (e.g., cell nuclei versus subwavelength organelles) but also on its shape (e.g., cells versus collagen fibers). The latter is directly related to the state of extracellular collagen matrix, which is often affected by early pathology. Hence, using polarimetric data can help to increase the accuracy of diagnosis.

Indirect imaging problems in biomedical optics generally require repeated evaluation of forward models of radiative transport, for which Monte Carlo is accurate yet computationally costly. We develop a novel approach to reduce this bottleneck which has significant implications for quantitative tomographic imaging in a variety of medical and industrial applications. Using Monte Carlo we compute a fully stochastic gradient of an objective function for a given imaging problem. Leveraging techniques from the machine learning community we then adaptively control the accuracy of this gradient throughout the iterative inversion scheme, in order to substantially reduce computational resources at each step. For example problems of Quantitative Photoacoustic Tomography and Ultrasound Modulated Optical Tomography, we demonstrate that solutions are attainable using a total computational expense that is comparable to (or less than) that which is required for a single high accuracy forward run of the same Monte Carlo model. This approach demonstrates significant computational savings when approaching the full non-linear inverse problem of optical property estimation using stochastic methods., Comment: 24 Pages, 11 figures

To detect small-scale changes in tissue with optical techniques, small sampling volumes are required. Single fiber reflectance (SFR) spectroscopy has a sampling depth of a few hundred micrometers. SFR spectroscopy uses a single fiber to emit and collect light. The only available model to determine optical properties with SFR spectroscopy was derived for tissues with modified Henyey-Greenstein phase functions. Previously, we demonstrated that this model is inadequate for other tissue phase functions. We develop a model to relate SFR measurements to scattering properties for a range of phase functions, in the absence of absorption. Since the source and detector overlap, the reflectance cannot be accurately described by diffusion theory alone: SFR measurements are subdiffuse. Therefore, we describe the reflectance as a combination of a diffuse and a semiballistic component. We use the model of Farrell et al. for the diffuse component, solved for an overlapping source and detector fiber. For the semiballistic component, we derive a new parameter, psb, which incorporates the integrals of the phase function over 1 deg in the backward direction and 23 deg in the forward direction. Our model predicts the reflectance with a median error of 2.1%, compared to 9.0% for the currently available model..

Since our previous publication of organ dose for the pediatric CT cohort in the UK, there have been questions about the magnitude of uncertainty in our dose estimates. We therefore quantified shared and unshared uncertainties in empirical CT parameters extracted from 1073 CT films (1978–2008) from 36 hospitals in the study and propagated these uncertainties into organ doses using Monte Carlo random sampling and NCICT organ dose calculator. The average of 500 median brain and marrow doses for the full cohort was 35 (95% confidence interval: 30–40) mGy and 6 (5–7) mGy, respectively. We estimated that shared uncertainty contributed ~99% of coefficient of variation of median brain doses in brain scans compared to unshared uncertainty (1% contribution). We found that the previous brain doses were slightly underestimated for 1990 compared to the results in the current study due to the revised CTDI models based on CT films.

The optical properties of human tissues are an important parameter in medical diagnostics and therapy. The knowledge of these parameters can encourage the development of automated, computer-driven optical tissue analysis methods. We determine the absorption coefficient μa and scattering coefficient μs′ of different tissue types obtained during parotidectomy in the wavelength range of 250 to 800 nm. These values are determined by high precision integrating sphere measurements in combination with an optimized inverse Monte Carlo simulation. To conserve the optical behavior of living tissues, the optical spectroscopy measurements are performed immediately after tissue removal. Our study includes fresh samples of the ear, nose, and throat (ENT) region, as muscle tissue, nervous tissue, white adipose tissue, stromal tissue, parotid gland, and tumorous tissue of five patients. The measured behavior of adipose corresponds well with the literature, which sustains the applied method. It is shown that muscle is well supplied with blood as it features the same characteristic peaks at 430 and 555 nm in the absorption curve. The parameter μs′ decreases for all tissue types above 570 nm. The accuracy is adequate for the purposes of providing μa and μs′ of different human tissue types as muscle, fat, nerve, or gland tissue, which are embedded in large complex structures such as in the ENT area. It becomes possible for the first time to present reasonable results for the optical behavior of human soft tissue located in the ENT area and in the near-UV, visual, and near-infrared areas.

Near-infrared spectroscopy (NIRS) to monitor muscle oxygen saturation (SmO2) is rapidly expanding into applied sports settings. However, the technology is limited due to its inability to convey quantifiable values. A test battery to assess reliability and validity of a 0% to 100% scale modeled by a commercially available NIRS device was established. This test battery applies a commonly used technique, the arterial occlusion method (AOM) to assess repeatability, reproducibility, and face validity. A total of 22 participants completed the test battery to scrutinize the 0% to 100% scale provided by the device. All participants underwent repeated AOM tests in passive and active conditions. The SmO2 minimum and SmO2 maximum values were obtained from the AOM and were used in the subsequent analysis. Repeatability and reproducibility were tested for equivalency and Bland–Altman plots were generated. Face validity was assessed by testing SmO2 values against an a priori defined threshold for mixed venous blood during AOM response. The device exhibits an appropriately functional 0% to 100% scale that is reliable in terms of repeatability and reproducibility. Under the conditions applied in the test battery design, the device is considered valid for application in sports.

The mesh-based Monte Carlo (MMC) algorithm is increasingly used as the gold-standard for developing new biophotonics modeling techniques in 3-D complex tissues, including both diffusion-based and various Monte Carlo (MC)-based methods. Compared to multilayered and voxel-based MCs, MMC can utilize tetrahedral meshes to gain improved anatomical accuracy but also results in higher computational and memory demands. Previous attempts of accelerating MMC using graphics processing units (GPUs) have yielded limited performance improvement and are not publicly available. We report a highly efficient MMC—MMCL—using the OpenCL heterogeneous computing framework and demonstrate a speedup ratio up to 420× compared to state-of-the-art single-threaded CPU simulations. The MMCL simulator supports almost all advanced features found in our widely disseminated MMC software, such as support for a dozen of complex source forms, wide-field detectors, boundary reflection, photon replay, and storing a rich set of detected photon information. Furthermore, this tool supports a wide range of GPUs/CPUs across vendors and is freely available with full source codes and benchmark suites at http://mcx.space/#mmc.

As photoacoustic imaging (PAI) technology matures, computational modeling will increasingly represent a critical tool for facilitating clinical translation through predictive simulation of real-world performance under a wide range of device and biological conditions. While modeling currently offers a rapid, inexpensive tool for device development and prediction of fundamental image quality metrics (e.g., spatial resolution and contrast ratio), rigorous verification and validation will be required of models used to provide regulatory-grade data that effectively complements and/or replaces in vivo testing. To address methods for establishing model credibility, we developed an integrated computational model of PAI by coupling a previously developed three-dimensional Monte Carlo model of tissue light transport with a two-dimensional (2D) acoustic wave propagation model implemented in the well-known k-Wave toolbox. We then evaluated ability of the model to predict basic image quality metrics by applying standardized verification and validation principles for computational models. The model was verified against published simulation data and validated against phantom experiments using a custom PAI system. Furthermore, we used the model to conduct a parametric study of optical and acoustic design parameters. Results suggest that computationally economical 2D acoustic models can adequately predict spatial resolution, but metrics such as signal-to-noise ratio and penetration depth were difficult to replicate due to challenges in modeling strong clutter observed in experimental images. Parametric studies provided quantitative insight into complex relationships between transducer characteristics and image quality as well as optimal selection of optical beam geometry to ensure adequate image uniformity. Multidomain PAI simulation tools provide high-quality tools to aid device development and prediction of real-world performance, but further work is needed to improve model fidelity, especially in reproducing image noise and clutter.

Despite decades of epidemiological research, it remains uncertain whether ionizing radiation can cause lymphomas. Most epidemiological studies of lymphoma risk following non-uniform exposure used dose to red bone marrow (RBM), constituting a small fraction of the lymphocytes, as a surrogate of dose to the lymphocytes. We developed a method to estimate dose to the lymphocytes using the reference distribution of lymphocytes throughout the body and Monte Carlo simulations of computational human phantoms. We applied our method to estimating lymphocyte doses for a pediatric CT patient cohort in the United Kingdom. Estimated dose to the RBM was greater than lymphocyte dose for most scan types (up to 2.6-fold higher, a 5-year-old brain scan) except abdomen scan (RBM dose was about half the lymphocyte dose, a 5-year-old abdomen scan). The lymphocyte dose in the UK cohort showed that T-spine and whole body scans delivered the highest lymphocyte doses (up to 22.4 mGy).

In recent decades, applications of single photon emission computed tomography and positron emission tomography in clinical neuroimaging have markedly increased. In this study, we developed a series of Korean computational head phantoms with detailed cranial substructures for 6-, 9-, 12- and 15-year-old children and adult by non-uniformly adjusting a template head phantom to match the Korean standard head dimensions. The Korean head phantoms were coupled with a Monte Carlo transport code to calculate age-dependent specific absorbed fraction (SAF) for the combination of 10 source and target regions and mono-energetic photons ranging from 0.01 to 4 MeV. Compared to the adult phantom, the 6-y phantom showed up to 1.4-fold greater self-absorption SAF (cerebellum) and up to 1.8-fold greater cross-irradiation SAF (cerebellum < eye balls). With addition of electron SAFs in the future, our photon SAF data will facilitate dose calculations for various cranial substructures in patients undergoing cranial neuroimaging procedures.

Photoacoustic imaging (PAI) provides high-resolution and high-optical-contrast imaging beyond optical diffusion limit. Further improvement in imaging depth has been achieved by using near-infrared window-I (NIR-I, 700 to 900 nm) for illumination, due to lower scattering and absorption by tissues in this wavelength range. Recently, near-infrared window-II (NIR-II, 900 to 1700 nm) has been explored for PAI. We studied the imaging depths in biological tissues for different illumination wavelengths in visible, NIR-I, and NIR-II regions using Monte Carlo (MC) simulations and validated with experimental results. MC simulations were done to compute fluence in tissue, absorbance in blood vessel, and in a spherical absorber (mimicking sentinel lymph node) embedded at different depths in breast tissue. Photoacoustic tomography and acoustic resolution photoacoustic microscopy experiments were conducted to validate the MC results. We demonstrate that maximum imaging depth is achieved by wavelengths in NIR-I window (∼800 nm) when the energy density deposited is same for all wavelengths. However, illumination using wavelengths around 1064 nm (NIR-II window) gives the maximum imaging depth when the energy density deposited is proportional to maximum permissible exposure (MPE) at corresponding wavelength. These results show that it is the higher MPE of NIR-II window that helps in increasing the PAI depth for chromophores embedded in breast tissue. NMRC (Natl Medical Research Council, S’pore) Published version

Approximately 50% of all colorectal cancer (CRC) patients will develop metastasis to the liver. 90Y selective internal radiation therapy (SIRT) is an established treatment for metastatic CRC. There is still a fundamental lack of understanding regarding the radiobiology underlying the dose response. This study was designed to determine the radiosensitivity of two CRC cell lines (DLD-1 and HT-29) to 90Y β − radiation exposure, and thus the relative effectiveness of 90Y SIRT in relation to external beam radiotherapy (EBRT). A 90Y-source dish was sandwiched between culture dishes to irradiate DLD-1 or HT-29 cells for a period of 6 d. Cell survival was determined by clonogenic assay. Dose absorbed per 90Y disintegration was calculated using the PENELOPE Monte Carlo code. PENELOPE simulations were benchmarked against relative dose measurements using EBT3 GAFchromic™ film. Statistical regression based on the linear-quadratic model was used to determine the radiosensitivity parameters and using R. These results were compared to radiosensitivity parameters determined for 6 MV clinical x-rays and 137Cs γ-ray exposure. Equivalent dose of EBRT in 2 Gy ( ) and 10 Gy ( ) fractions were derived for 90Y dose. HT-29 cells were more radioresistant than DLD-1 for all treatment modalities. Radiosensitivity parameters determined for 6 MV x-rays and 137Cs γ-ray were equivalent for both cell lines. The ratio for 90Y β −-particle exposure was over an order of magnitude higher than the other two modalities due to protraction of dose delivery. Consequently, an 90Y SIRT absorbed dose of 60 Gy equates to an of 28.7 and 54.5 Gy and an of 17.6 and 19.3 Gy for DLD-1 and HT-29 cell lines, respectively. We derived radiosensitivity parameters for two CRC cell lines exposed to 90Y β −-particles, 6 MV x-rays, and 137Cs γ-ray irradiation. These radiobiological parameters are critical to understanding the dose response of CRC lesions and ultimately informs the efficacy of 90Y SIRT relative to other radiation therapy modalities.

Quantitative photoacoustic tomography aims to recover maps of the local concentrations of tissue chromophores from multispectral images. While model-based inversion schemes are promising approaches, major challenges to their practical implementation include the unknown fluence distribution and the scale of the inverse problem. We describe an inversion scheme based on a radiance Monte Carlo model and an adjoint-assisted gradient optimization that incorporates fluence-dependent step sizes and adaptive moment estimation. The inversion is shown to recover absolute chromophore concentrations, blood oxygen saturation, and the Grüneisen parameter from in silico three-dimensional phantom images for different radiance approximations. The scattering coefficient is assumed to be homogeneous and known a priori.

Although the luminescence of water at a lower energy than the Cerenkov-light (CL) threshold has been found for various types of radiation, the fractions of the luminescence of water to the total produced light have not been obvious for radiations at a higher energy than the CL threshold because it is difficult to separate these two types of light. Thus, we used a Monte Carlo simulation to estimate the fractions of the luminescence of water for various types of radiation at a higher energy than the CL threshold to confirm the major component of the produced light. After we confirmed that the estimated light production of the luminescence of water could adequately simulate the experimental results, we calculated the produced light photons of this luminescence and the CL from water for protons (170 MeV), carbon ions (330 MeV/n), high-energy x-ray (6 MV) from a linear accelerator (LINAC), high-energy electrons (9 MeV) from LINAC, positrons (F-18, C-11, O-15, and N-13), and high-energy gamma photon radionuclides (Co-60). For protons, the major fraction of the produced light was the luminescence of water in addition to the CL from the prompt gamma photons produced by the nuclear interactions. For carbon ions, the major fraction of the produced light was the luminescence of water and the CL produced by the secondary electrons in addition to the prompt gamma photons produced by the nuclear interactions. For high-energy x-ray and electrons from LINAC, the fractions of luminescence of water were ∼0.1% to 0.2%. The fractions of luminescence of water for positrons were 0.2% to 1.5% and that for Co-60 was 0.4%. We conclude that the major fractions of light produced from x-ray and electrons from LINAC, positron radionuclides, and the Co-60 source are CL, with fractions of the luminescence of water from

The PeriFlux 6000 EPOS system combines diffuse reflectance spectroscopy (DRS) and laser Doppler flowmetry (LDF) for the assessment of oxygen saturation (expressed in percentage), red blood cell (RBC) tissue fraction (expressed as volume fraction, %RBC), and perfusion (%RBC × mm / s) in the microcirculation. It also allows the possibility of separating the perfusion into three speed regions (0 to 1, 1 to 10, and10 mm / s). We evaluate the speed-resolved perfusion components, i.e., the relative amount of perfusion within each speed region, using a blood-flow phantom. Human blood was pumped through microtubes with an inner diameter of 0.15 mm. Measured DRS and LDF spectra were compared to Monte Carlo-simulated spectra in an optimization routine, giving the best-fit parameters describing the measured spectra. The root-mean-square error for each of the three speed components (0 to 1, 1 to 10, and10 mm / s, respectively) when describing the blood-flow speed in the microtubes was 2.9%, 8.1%, and 7.7%. The presented results show that the system can accurately discriminate blood perfusion originating from different blood-flow speeds, which may enable improved measurement of healthy and dysfunctional microcirculatory flow.

We compare previously reported benzoporphyrin derivative (BPD)-mediated photodynamic therapy (PDT) results for reactive singlet oxygen concentration (also called singlet oxygen dose) on mice with simulations using a computational device, Dosie™, that calculates light transport and photokinetics for PDT in near real-time. The two sets of results are consistent and validate the use of the device in PDT treatment planning to predict BPD-mediated PDT outcomes in mice animal studies based on singlet oxygen dose, which showed a much better correlation with the cure index than the conventional light dose.

An approach based on track-structure calculations has been developed to take account of artefacts occurring during γ-H2AX foci detection in 2D images of samples analyzed through immunocytochemistry. The need of this works stems from the observed saturation in foci yields measured after X-ray doses higher than few grays, hindering an unambiguous quantification of DNA damage and of radiation effectiveness. The proposed modelling approach allows to simulate the observer's point of view for foci scoring, mimicking the selection of a slice Δz of the cell nucleus due to the microscope depth of field, and applying a clustering algorithm to group together damages within a resolution parameter r. Calculation results were benchmarked with experimental measurements at an early time-point for mouse breast cancer cells, irradiated with X-ray doses in the range 0-5 Gy. The model is able to reproduce the saturation in experimental data.

Pulsed dye laser irradiation in the wavelength range of 585 to 600nm is currently the gold standard for treatment of port-wine stains (PWSs). However, this treatment method is often ineffective for deeply seated blood vessels and in individuals with moderate to heavy pigmentation. Use of optical particles doped with the FDA-approved near-infrared (NIR) absorber, indocyanine green (ICG), can potentially provide an effective method to overcome these limitations. Herein, we theoretically investigate the effectiveness of particles derived from erythrocytes, which contain ICG, in mediating photothermal destruction of PWS blood vessels. We refer to these particles as NIR erythrocyte-derived transducers (NETs). Our theoretical model consists of a Monte Carlo algorithm to estimate the volumetric energy deposition, a finite elements approach to solve the heat diffusion equation, and a damage integral based on an Arrhenius relationship to quantify tissue damage. The model geometries include simulated PWS blood vessels as well as actual human PWS blood vessels plexus obtained by the optical coherence tomography. Our simulation results indicate that blood vessels containing micron- or nano-sized NETs and irradiated at 755nm have higher levels of photothermal damage as compared to blood vessels without NETs irradiated at 585nm. Blood vessels containing micron-sized NETs also showed higher photothermal damage than blood vessels containing nano-sized NETs. The theoretical model presented can be used in guiding the fabrication of NETs with patient-specific optical properties to allow for personalized treatment based on the depth and size of blood vessels as well as the pigmentation of the individual's skin.

High-Z nano materials have been previously shown to increase the amount of dose deposition within the tumour due to an increase in secondary electrons. This study evaluates the effects of high-Z nano materials in combination with protons, and the impact of proton energy, nanoparticle material and concentration. These effects were studied in silico through Monte Carlo simulation and experimentally through a phantom study, with particular attention to macroscale changes to the Bragg peak in the presence of nanoparticles. Three nanoparticle materials were simulated (gold, silver and platinum) at three concentrations (0.01, 0.1 and 6.5 mg ml(-1)) at two clinical proton energies (60 and 226 MeV). Simulations were verified experimentally using Gafchromic film measurements of gold nanoparticles suspended in water at two available high concentrations (5.5 mg ml(-1) and 1.1 mg ml(-1)). A significant change to Bragg peak features was evident, where at 226 MeV and 6.5 mg ml(-1), simulations of gold showed a 4.7 mm longitudinal shift of the distal edge and experimentally at 5.5 mg ml(-1), a shift of 2.2 mm. Simulations showed this effect to be material dependent, where platinum having the highest physical density caused the greatest shift with increasing concentration. A dose enhancement of 6% ± 0.05 and 5% ± 0.15 (60 MeV and 226 MeV, respectively) was evident with gold at 6.5 mg ml(-1) to water alone, compared to the 21% ± 0.53 observed experimentally as dose to film with 5.5 mg ml(-1) of gold nanoparticles suspended in water at 226 MeV. The introduction of nanoparticles has strong potential to enhance dose in proton therapy, however the changes to the Bragg peak distribution that occur with high concentrations need to be accounted for to ensure tumour coverage.

The total emission control target of water pollutants (e.g., COD and NH 4 –N) for a certain industrial sector can be predicted and analysed using the popular technology-based bottom-up modelling. However, this methodology has obvious uncertainty regarding the attainment of mitigation targets. The primary uncertainty comes from macro-production, pollutant reduction roadmap, and technical parameters. This research takes the paper and pulp industry in China as an example, and builds 5 mitigation scenarios via different combinations of raw material structure, scale structure, procedure mitigation technology, and end-of-pipe treatment technology. Using the methodology of uncertainty analysis via Monte Carlo, random sampling was conducted over a hundred thousand times. According to key parameters, sensitive parameters that impact total emission control targets such as industrial output, technique structure, cleaner production technology, and end-of-pipe treatment technology are discussed in this article. It appears that scenario uncertainty has a larger influence on COD emission than NH 4 –N, hence it is recommended that a looser total emission control target for COD is necessary to increase its feasibility and availability while maintaining the status quo of NH 4 –N. Consequently, from uncertainty analysis, this research recognizes the sensitive products, techniques, and technologies affecting industrial water pollution.

Effective dose from computed tomography (CT) examinations is usually estimated using the scanner-provided dose-length product and using conversion factors, also known as k-factors, which correspond to scan regions and differ by age according to five categories: 0, 1, 5, 10 y and adult. However, patients often deviate from the standard body size on which the conversion factor is based. In this study, a method for deriving body size-specific k-factors is presented, which can be determined from a simple regression curve based on patient diameter at the centre of the scan range. Using the International Commission on Radiological Protection reference paediatric and adult computational phantoms paired with Monte Carlo simulation of CT X-ray beams, the authors derived a regression-based k-factor model for the following CT scan types: head–neck, head, neck, chest, abdomen, pelvis, abdomen–pelvis (AP) and chest–abdomen–pelvis (CAP). The resulting regression functions were applied to a total of 105 paediatric and 279 adult CT scans randomly sampled from patients who underwent chest, AP and CAP scans at the National Institutes of Health Clinical Center. The authors have calculated and compared the effective doses derived from the conventional age-specific k-factors with the values computed using their body size-specific k-factor. They found that by using the age-specific k-factor, paediatric patients tend to have underestimates (up to 3-fold) of effective dose, while underweight and overweight adult patients tend to have underestimates (up to 2.6-fold) and overestimates (up to 4.6-fold) of effective dose, respectively, compared with the effective dose determined from their body size-dependent factors. The authors present these size-specific k-factors as an alternative to the existing age-specific factors. The body size-specific k-factor will assess effective dose more precisely and on a more individual level than the conventional age-specific k-factors and, hence, improve awareness of the true exposure, which is important for the clinical community to understand.

Significance: In laser therapy and diagnosis of skin diseases, the irradiated light distribution, which is determined by the absorption coefficient μa and reduced scattering coefficient μs′ of the epidermis, dermis, and subcutaneous fat, affects the treatment outcome and diagnosis accuracy. Although values for μa and μs′ have been reported, detailed analysis for Asian skin tissues is still lacking. Aim: We present μa and μs′ measurements of Asian skin tissues in the 400- to 1100-nm wavelength range for evaluating optical penetration depth and energy deposition. Approach: The measurements with Asian human skin samples are performed employing a double integrating sphere spectrometric system and an inverse Monte Carlo technique. Using the measured parameters, the optical penetration depth and energy deposition are quantitatively analyzed. Results: The μa of the epidermis layer varies among different ethnic groups, while the μa of the other layers and the μs′ of all of the layers exhibit almost no differences. The analysis reveals that the optical penetration depth and the energy deposition affect the photodynamic therapy treatment depth and the heat production in skin tissue, respectively. Conclusions: The experimentally measured values of μa and μs′ for Asian skin tissues are presented, and the light behavior in Asian skin tissues is analyzed using a layered tissue model.

The novel coronavirus pneumonia triggered by COVID-19 is now raging the whole world. As a rapid and reliable killing COVID-19 method in industry, electron beam irradiation can interact with virus molecules and destroy their activity. With the unexpected appearance and quickly spreading of the virus, it is urgently necessary to figure out the mechanism of electron beam irradiation on COVID-19. In this study, we establish a virus structure and molecule model based on the detected gene sequence of Wuhan patient, and calculate irradiated electron interaction with virus atoms via a Monte Carlo simulation that track each elastic and inelastic collision of all electrons. The characteristics of irradiation damage on COVID-19, atoms’ ionizations and electron energy losses are calculated and analyzed with regions. We simulate the different situations of incident electron energy for evaluating the influence of incident energy on virus damage. It is found that under the major protecting of an envelope protein layer, the inner RNA suffers the minimal damage. The damage for a ∼100-nm-diameter virus molecule is not always enhanced by irradiation energy monotonicity, for COVID-19, the irradiation electron energy of the strongest energy loss damage is 2 keV.

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/.

Significance: Functional near-infrared spectroscopy (fNIRS) has become an important research tool in studying human brains. Accurate quantification of brain activities via fNIRS relies upon solving computational models that simulate the transport of photons through complex anatomy. Aim: We aim to highlight the importance of accurate anatomical modeling in the context of fNIRS and propose a robust method for creating high-quality brain/full-head tetrahedral mesh models for neuroimaging analysis. Approach: We have developed a surface-based brain meshing pipeline that can produce significantly better brain mesh models, compared to conventional meshing techniques. It can convert segmented volumetric brain scans into multilayered surfaces and tetrahedral mesh models, with typical processing times of only a few minutes and broad utilities, such as in Monte Carlo or finite-element-based photon simulations for fNIRS studies. Results: A variety of high-quality brain mesh models have been successfully generated by processing publicly available brain atlases. In addition, we compare three brain anatomical models—the voxel-based brain segmentation, tetrahedral brain mesh, and layered-slab brain model—and demonstrate noticeable discrepancies in brain partial pathlengths when using approximated brain anatomies, ranging between −1.5% to 23% with the voxelated brain and 36% to 166% with the layered-slab brain. Conclusion: The generation and utility of high-quality brain meshes can lead to more accurate brain quantification in fNIRS studies. Our open-source meshing toolboxes “Brain2Mesh” and “Iso2Mesh” are freely available at http://mcx.space/brain2mesh.

Combined ultrasound and photoacoustic imaging systems are being developed for biomedical and clinical applications. One common probe configuration is to use a linear transducer array with external light delivery to produce coregistered ultrasound and photoacoustic images. The diagnostic capability of these systems is dependent on the effectiveness of light delivery to the imaging target. We use Monte Carlo modeling to investigate the optimal design geometry of an integrated probe. Simulations are conducted with multiple tissue compositions and wavelengths. The effect of a skin layer with the thickness of a mouse or a human is also considered. The model was validated using a tissue-mimicking gelatin phantom and corresponding Monte Carlo simulations. The optimal illumination angle is shallower with human skin thickness, whereas intermediate angles are ideal with mouse skin thickness. The effect of skin thickness explains differences in the results of prior work. The simulations also indicate that even with identical hardware and imaging parameters, light delivery will be up to 3× smaller in humans than in mice, due to the increased scattering from thicker skin. Our findings have clear implications for the many researchers using mice to test and develop imaging methods for clinical translation.

Laser speckle contrast imaging (LSCI) enables video rate imaging of blood flow. However, its relation to tissue blood perfusion is nonlinear and depends strongly on exposure time. By contrast, the perfusion estimate from the slower laser Doppler flowmetry (LDF) technique has a relationship to blood perfusion that is better understood. Multiexposure LSCI (MELSCI) enables a perfusion estimate closer to the actual perfusion than that using a single exposure time. We present and evaluate a method that utilizes contrasts from seven exposure times between 1 and 64 ms to calculate a perfusion estimate that resembles the perfusion estimate from LDF. The method is based on artificial neural networks (ANN) for fast and accurate processing of MELSCI contrasts to perfusion. The networks are trained using modeling of Doppler histograms and speckle contrasts from tissue models. The importance of accounting for noise is demonstrated. Results show that by using ANN, MELSCI data can be processed to LDF perfusion with high accuracy, with a correlation coefficient R = 1.000 for noise-free data, R = 0.993 when a moderate degree of noise is present, and R = 0.995 for in vivo data from an occlusion-release experiment.

This study developed a computationally efficient and easy-to-implement analytical model to estimate the equivalent dose from secondary neutrons originating in the bodies (‘internal neutrons’) of children receiving intracranial proton radiotherapy. A two-term double-Gaussian mathematical model was fit to previously published internal neutron equivalent dose per therapeutic absorbed dose versus distance from the field edge calculated using Monte Carlo simulations. The model was trained using three intracranial proton fields of a 9-year-old girl. The resulting model was tested against two intracranial fields of a 10-year-old boy by comparing the mean doses in organs at risk of a radiogenic cancer estimated by the model versus those previously calculated by Monte Carlo. On average, the model reproduced the internal neutron organ doses in the 10-year-old boy within 13.5% of the Monte Carlo at 3–10 cm from the field edge and within a factor of 2 of the Monte Carlo at 10–20 cm from the field edge. Beyond 20 cm, the model poorly estimated H/DRx, however, the values were very small, at