Reconstruction of light absorption changes in the human head using analytically computed photon partial pathlengths in layered media.

Functional near infrared spectroscopy has been used in recent decades to sense and quantify changes in hemoglobin concentrations in the human brain. This noninvasive technique can deliver useful information concerning brain cortex activation associated with different motor/cognitive tasks or external stimuli. This is usually accomplished by considering the human head as a homogeneous medium; however, this approach does not explicitly take into account the detailed layered structure of the head, and thus, extracerebral signals can mask those arising at the cortex level. This work improves this situation by considering layered models of the human head during reconstruction of the absorption changes in layered media. To this end, analytically calculated mean partial pathlengths of photons are used, which guarantees fast and simple implementation in real-time applications. Results obtained from synthetic data generated by Monte Carlo simulations in two- and four-layered turbid media suggest that a layered description of the human head greatly outperforms typical homogeneous reconstructions, with errors, in the first case, bounded up to ∼20% maximum, while in the second case, the error is usually larger than 75%. Experimental measurements on dynamic phantoms support this conclusion.

A review article on the dosimetry in intraluminal HDR brachytherapy of lung carcinoma.

Lung cancer is one of the most common cancers in the world. Intraluminal brachytherapy (BT) is one of the most adopted treatment modalities for lung malignancies with Ir-192 source in radiotherapy. In intraluminal BT, treatment delivery is required to be very accurate and precise with respect to the plan created in the treatment planning system (TPS). The BT dosimetry is necessary for better treatment outcomes. Therefore in this review article, some relevant studies were identified and analyzed for dosimetric outcomes in intraluminal BT in lung malignancies. The dosimetry in BT for plan verification is not presently in practice, which needs to be performed to check the variation between the planned and measured doses. The necessary dosimetric work done by the various researchers in intraluminal BT such as the Monte Carlo CYLTRAN code was used to calculate and measure the dose rate in any medium. Anthropomorphic phantom was used to measure doses at some distance from the source with Thermo luminescence dosimeters (TLDs). The dosimetric influence of air passage in the bronchus was evaluated with the GEANT4 Monte Carlo method. A pinpoint chamber was used to measure and quantify the impact of inhomogeneity in wax phantom for the Ir-192 source. The Gafchromic films and Monte Carlo methods were used to find the phantom and heterogeneities, which were found to underestimate the dose for the lungs and overestimated for the bones in TPS. The exact tool to quantify the variation in planned and delivered doses should be cost-effective and easy to use possibly with tissue equivalent phantoms and Gafchromic films in lung malignancies treatment.

Reproducibility study of Monte Carlo simulations for nanoparticle dose enhancement and biological modeling of cell survival curves.

Nanoparticle-derived radiosensitization has been investigated by several groups using Monte Carlo simulations and biological modeling. In this work we replicated the physical simulation and biological modeling of previously published research for 50 nm gold nanoparticles irradiated with monoenergetic photons, various 250 kVp photon spectra, and spread-out Bragg peak (SOBP) protons. Monte Carlo simulations were performed using TOPAS and used condensed history Penelope low energy physics models for macroscopic dose deposition and interaction with the nanoparticle; simulation of the microscopic dose deposition from nanoparticle secondaries was performed using Geant4-DNA track structure physics. Biological modeling of survival fractions was performed using a local effect model-type approach for MDA-MB-231 breast cancer cells. Physical simulation results agreed extraordinarily well at all distances (1 nm to 10µm from nanoparticle) for monoenergetic photons and SOBP protons in terms of dose per interaction, dose kernel ratio (often labeled dose enhancement factor), and secondary electron spectra. For 250 kVp photons the influence of the gold K-edge was investigated and found to appreciably affect the results. Calculated survival fractions similarly agreed well within one order of magnitude at macroscopic doses (i.e. without nanoparticle contribution) from 1 Gy to 10 Gy. Several 250 kVp spectra were tested to find one yielding closest agreement with previous results. This highlights the importance of a detailed description of the low energy (< 150 keV) component of photon spectra used forin-silico, as well asin-vitro, andin-vivostudies to ensure reproducibility of the experiments by the scientific community. Both, Monte Carlo simulations of physical interactions of the nanoparticle with photons and protons, as well as the biological modelling of cell survival curves agreed extraordinarily well with previously published data. Further investigation of the stochastic nature of nanoparticle radiosenstiziation is ongoing.

Multiple imputation methods for missing multilevel ordinal outcomes.

BACKGROUND: Multiple imputation (MI) is an established technique for handling missing data in observational studies. Joint modelling (JM) and fully conditional specification (FCS) are commonly used methods for imputing multilevel data. However, MI methods for multilevel ordinal outcome variables have not been well studied, especially when cluster size is informative on the outcome. The purpose of this study is to describe and compare different MI strategies for dealing with multilevel ordinal outcomes when informative cluster size (ICS) exists. METHODS: We conducted comprehensive Monte Carlo simulation studies to compare the performance of five strategies: complete case analysis (CCA), FCS, FCS+CS (including cluster size (CS) in the imputation model), JM, and JM+CS under various scenarios. We evaluated their performance using a proportional odds logistic regression model estimated with cluster weighted generalized estimating equations (CWGEE). RESULTS: The simulation results showed that including CS in the imputation model can significantly improve estimation accuracy when ICS exists. FCS provided more accurate and robust estimation than JM, followed by CCA for multilevel ordinal outcomes. We further applied these strategies to a real dental study to assess the association between metabolic syndrome and clinical attachment loss scores. The results based on FCS + CS indicated that the power of the analysis would increase after carrying out the appropriate MI strategy. CONCLUSIONS: MI is an effective tool to increase the accuracy and power of the downstream statistical analysis for missing ordinal outcomes. FCS slightly outperforms JM when imputing multilevel ordinal outcomes. When there is plausible ICS, we recommend including CS in the imputation phase.

Monitoring the skin structure during edema in vivo with spatially resolved diffuse reflectance spectroscopy.

Significance: Edema occurs in the course of various skin diseases. It manifests itself in changes in water concentrations in skin layers: dermis and hypodermis and their thicknesses. In medicine and cosmetology, objective tools are required to assess the skin's physiological parameters. The dynamics of edema and the skin of healthy volunteers were studied using spatially resolved diffuse reflectance spectroscopy (DRS) in conjunction with ultrasound (US). Aim: In this work, we have developed a method based on DRS with a spatial resolution (SR DRS), allowing us to simultaneously assess water content in the dermis, dermal thickness, and hypodermal thickness. Approach: An experimental investigation of histamine included edema using SR DRS under the control of US was conducted. An approach for skin parameter determination was studied and confirmed using Monte-Carlo simulation of diffuse reflectance spectra for a three-layered system with the varying dermis and hypodermis parameters. Results: It was shown that an interfiber distance of 1 mm yields a minimal relative error of water content determination in the dermis equal to 9.3%. The lowest error of hypodermal thickness estimation was achieved with the interfiber distance of 10 mm. Dermal thickness for a group of volunteers (7 participants, 21 measurement sites) was determined using SR DRS technique with an 8.3% error using machine learning approaches, taking measurements at multiple interfiber distances into account. Hypodermis thickness was determined with root mean squared error of 0.56 mm for the same group. Conclusions: This study demonstrates that measurement of the skin diffuse reflectance response at multiple distances makes it possible to determine the main parameters of the skin and will serve as the basis for the development and verification of an approach that works in a wide range of skin structure parameters.

ROptimus: a parallel general-purpose adaptive optimization engine.

MOTIVATION: Various computational biology calculations require a probabilistic optimization protocol to determine the parameters that capture the system at a desired state in the configurational space. Many existing methods excel at certain scenarios, but fail in others due, in part, to an inefficient exploration of the parameter space and easy trapping into local minima. Here, we developed a general-purpose optimization engine in R that can be plugged to any, simple or complex, modelling initiative through a few lucid interfacing functions, to perform a seamless optimization with rigorous parameter sampling. RESULTS: ROptimus features simulated annealing and replica exchange implementations equipped with adaptive thermoregulation to drive Monte Carlo optimization process in a flexible manner, through constrained acceptance frequency but unconstrained adaptive pseudo temperature regimens. We exemplify the applicability of our R optimizer to a diverse set of problems spanning data analyses and computational biology tasks. AVAILABILITY AND IMPLEMENTATION: ROptimus is written and implemented in R, and is freely available from CRAN (http://cran.r-project.org/web/packages/ROptimus/index.html) and GitHub (http://github.com/SahakyanLab/ROptimus).

A hybrid multi-particle approach to range assessment-based treatment verification in particle therapy.

Particle therapy (PT) used for cancer treatment can spare healthy tissue and reduce treatment toxicity. However, full exploitation of the dosimetric advantages of PT is not yet possible due to range uncertainties, warranting development of range-monitoring techniques. This study proposes a novel range-monitoring technique introducing the yet unexplored concept of simultaneous detection and imaging of fast neutrons and prompt-gamma rays produced in beam-tissue interactions. A quasi-monolithic organic detector array is proposed, and its feasibility for detecting range shifts in the context of proton therapy is explored through Monte Carlo simulations of realistic patient models and detector resolution effects. The results indicate that range shifts of [Formula: see text] can be detected at relatively low proton intensities ([Formula: see text] protons/spot) when spatial information obtained through imaging of both particle species are used simultaneously. This study lays the foundation for multi-particle detection and imaging systems in the context of range verification in PT.

Optimized Monte Carlo simulations for voxel-based internal dosimetry.

Objective.The scientific community has considered internal dosimetry by the Monte Carlo method the gold standard. However, there is a trade-off between simulation processing time and the statistical quality of the results that makes it a challenge to obtain accurate absorbed dose values in some situations, such as dose estimation in organs affected by cross-irradiation or limited computing power. Variance reduction techniques are used to reduce computational processing time without impairing the statistical quality of the results, such as tracking energy cutoff, secondary particle production threshold, and parallelism of different types of emissions from radionuclides.Approach.In this work, GATE Monte Carlo code and its variance reduction techniques were evaluated to calculateSvalues of organs from the international commission on radiological protection (ICRP) report 110 male phantom for the lutetium-177, iodine-131, yttrium-90, and radium-223 radionuclides. The results are compared with the data from the OpenDose collaboration.Main results.A cutoff of 5 MeV for local electron deposition and 2.0 mm of secondary particle production range resulted in a computational efficiency increase of 7.9 and 1.05 times, respectively. Simulation of ICRP 107 spectra-based source proved to be about 5 times more efficient when compared to a decay simulation usingG4RadioactiveDecay(Geant4-based radioactive decay processes). Track length estimator (TLE) and split exponential track length estimator (seTLE) techniques were used to calculate the absorbed dose of photon emissions, resulting in computational efficiency up to 29.4 and 62.5 times higher when compared to traditional simulations, respectively. In particular, the seTLE technique accelerates the simulation time by up to 1426 times, achieving a statistical uncertainty of 10% in volumes affected by cross-irradiation.Significance.The variance reduction techniques used in this work drastically reduced the simulation time and maintained the statistical quality of the calculated absorbed dose values, proving the feasibility of the use of the Monte Carlo method in internal dosimetry under challenging situations and making it viable for clinical routine or web applications.

The general-purpose Geant4 Monte Carlo toolkit and its Geant4-DNA extension to investigate mechanisms underlying the FLASH effect in radiotherapy: Current status and challenges.

FLASH radiotherapy is a promising approach to cancer treatment that offers several advantages over conventional radiotherapy. With this novel technique, high doses of radiation are delivered in a short period of time, inducing the so-called FLASH effect - a phenomenon characterized by healthy tissue sparing without alteration of tumor control. The mechanisms behind the FLASH effect remain unknown. One way to approach this problem is to gain insight into the initial parameters that can distinguish FLASH from conventional irradiation by simulating particle transport in aqueous media using the general-purpose Geant4 Monte Carlo toolkit and its Geant4-DNA extension. This review article discusses the current status of Geant4 and Geant4-DNA simulations to investigate mechanisms underlying the FLASH effect, as well as the challenges faced in this research field. One of the primary challenges is to accurately simulate the experimental irradiation parameters. Another challenge is the temporal extension of the simulations. This review also focuses on two hypotheses to explain the FLASH effect - namely the oxygen depletion hypothesis and the inter-track interactions hypothesis - and discusses how the Geant4 toolkit can be used to investigate them. The aim of this review is to provide an overview of Geant4 and Geant4-DNA simulations for FLASH radiotherapy and to highlight the challenges that need to be overcome in order to better study the FLASH effect.

PyMCGPU-IR Monte Carlo code test for occupational dosimetry.

PyMCGPU-IR is an innovative occupational dose monitoring tool for interventional radiology procedures. It reads the radiation data from the Radiation Dose Structured Report of the procedure and combines this information with the position of the monitored worker recorded using a 3D camera system. This information is used as an input file for the fast Monte Carlo radiation transport code MCGPU-IR in order to assess the organ doses, Hp(10) and Hp(0.07), as well as the effective dose. In this study, Hp(10) measurements of the first operator during an endovascular aortic aneurysm repair procedure and a coronary angiography using a ceiling suspended shield are compared to PyMCGPU-IR calculations. Differences in the two reported examples are found to be within 15%, which is considered as being very satisfactory. The study highlights the promising advantages of PyMCGPU-IR, although there are still several improvements that need to be implemented before its final clinical use.

Radiation protection challenges for the Large Hadron Collider upgrade.

In the context of the so-called Long Shutdown 3 (2026-2028), the Large Hadron Collider will be upgraded to the High-Luminosity Large Hadron Collider, allowing for approximately five more instantaneous collisions. The upgrade, maintenance and decommissioning of equipment will be mainly performed in the experimental insertions of Points 1 and 5, requiring to perform multiple interventions in high-residual radiation environment. This poses complex radiological challenges that the CERN Radiation Protection group is called to address. Radiation protection studies are performed to plan and optimise (ALARA) these future interventions using the advanced Monte Carlo techniques and tools such as FLUKA, ActiWiz, SESAME and the FCC method. This paper aims to provide an overview of the studies conducted to estimate the residual radiation field in the experimental insertions, the activation levels in terms of multiple of the Swiss clearance limits/specific activity and to provide preliminary considerations on the upgrade/decommissioning of key equipment.

Radiation protection challenges in the upgrade, autopsy and disposal of the LHC beam dump.

The upgrade of the Large Hadron Collider spare beam dumps (Target Dump External, TDE) and the autopsy of the old operational TDE required to perform several activities in a high-radiation environment posing significant radiation protection challenges due to the residual activation of the equipment. To ensure high safety standards and to respect the ALARA principle, these challenges were addressed using the advanced Monte Carlo techniques to predict the residual ambient dose equivalent rate and the radionuclide inventory at different steps of the interventions. The CERN HSE-RP group makes extensive use of the FLUKA and ActiWiz codes to produce accurate estimates. This work aims to provide an overview of the radiation protection studies to optimise the interventions (ALARA) and to reduce the radiological risk for personnel and environment.

Operational radiation protection challenges for the LHC experiments.

Radiation protection physicists at CERN are often required to assess residual activation for the Large Hadron Collider (LHC) experiments during stop periods in order to ensure adequate optimization during planned exposure situations and to establish proper procedures for the radiological control of materials. Given the complexity of the facilities and of the high-energy and mixed fields inducing the activation, Monte Carlo transport codes are an essential tool to simulate both prompt and residual radiation. The present work highlights the challenges of assessing residual dose rates for the LHC experiments in shutdown configurations and of establishing residual activation zonings. For the latter, a method based on fluence conversion coefficients was developed and is efficiently employed. The practical example of the assessment of the activation of the 600 tons of austenitic stainless steel in the future Compact Muon Solenoid (CMS) High Granularity Calorimeter will be used to demonstrate how these challenges are dealt with and the capabilities of the method developed.

Exact expressions and numerical evaluation of average evolvability measures for characterizing and comparing [Formula: see text] matrices.

Theory predicts that the additive genetic covariance ([Formula: see text]) matrix determines a population's short-term (in)ability to respond to directional selection-evolvability in the Hansen-Houle sense-which is typically quantified and compared via certain scalar indices called evolvability measures. Often, interest is in obtaining the averages of these measures across all possible selection gradients, but explicit formulae for most of these average measures have not been known. Previous authors relied either on approximations by the delta method, whose accuracy is generally unknown, or Monte Carlo evaluations (including the random skewers analysis), which necessarily involve random fluctuations. This study presents new, exact expressions for the average conditional evolvability, average autonomy, average respondability, average flexibility, average response difference, and average response correlation, utilizing their mathematical structures as ratios of quadratic forms. The new expressions are infinite series involving top-order zonal and invariant polynomials of matrix arguments, and can be numerically evaluated as their partial sums with, for some measures, known error bounds. Whenever these partial sums numerically converge within reasonable computational time and memory, they will replace the previous approximate methods. In addition, new expressions are derived for the average measures under a general normal distribution for the selection gradient, extending the applicability of these measures into a substantially broader class of selection regimes.

Bayesian compositional regression with microbiome features via variational inference.

The microbiome plays a key role in the health of the human body. Interest often lies in finding features of the microbiome, alongside other covariates, which are associated with a phenotype of interest. One important property of microbiome data, which is often overlooked, is its compositionality as it can only provide information about the relative abundance of its constituting components. Typically, these proportions vary by several orders of magnitude in datasets of high dimensions. To address these challenges we develop a Bayesian hierarchical linear log-contrast model which is estimated by mean field Monte-Carlo co-ordinate ascent variational inference (CAVI-MC) and easily scales to high dimensional data. We use novel priors which account for the large differences in scale and constrained parameter space associated with the compositional covariates. A reversible jump Monte Carlo Markov chain guided by the data through univariate approximations of the variational posterior probability of inclusion, with proposal parameters informed by approximating variational densities via auxiliary parameters, is used to estimate intractable marginal expectations. We demonstrate that our proposed Bayesian method performs favourably against existing frequentist state of the art compositional data analysis methods. We then apply the CAVI-MC to the analysis of real data exploring the relationship of the gut microbiome to body mass index.

Analytical modeling and Monte Carlo simulations of multi-parallel slit and knife-edge slit prompt gamma cameras.

Purpose.Present and validate an analytical model (AM) to calculate efficiency and spatial resolution of multi-parallel slit (MPS) and knife-edge slit (KES) cameras in the context of prompt gamma (PG) imaging in proton therapy, as well as perform a fair comparison between two prototypes of these cameras with their design specifications.Materials and methods.Monte Carlo (MC) simulations with perfect (ideal) conditions were performed to validate the proposed AM, as well as simulations in realistic conditions for the comparison of both prototypes. The spatial resolution obtained from simulations was derived from reconstructed PG profiles. The falloff retrieval precision (FRP) was quantified based on the variability of PG profiles from 50 different realizations.Results.The AM shows that KES and MPS designs fulfilling 'MPS-KES similar conditions' should have very close actual performances if the KES slit width corresponds to the half of the MPS slit width. Reconstructed PG profiles from simulated data with both cameras were used to compute the efficiency and spatial resolutions to compare against the model predictions. The FRP of both cameras was calculated with realistic detection conditions for beams with 107, 108and 109incident protons. A good agreement was found between the values predicted by the AM and those obtained from MC simulations (relative deviations of the order of 5%).Conclusion.The MPS camera outperforms the KES camera with their design specifications in realistic conditions and both systems can reach millimetric precision in the determination of the falloff position with 108or more initial protons.

Bayesian composite quantile regression for the single-index model.

By using a Gaussian process prior and a location-scale mixture representation of the asymmetric Laplace distribution, we develop a Bayesian analysis for the composite quantile single-index regression model. The posterior distributions for the unknown parameters are derived, and the Markov chain Monte Carlo sampling algorithms are also given. The proposed method is illustrated by three simulation examples and a real dataset.

Validation of RayStation Monte Carlo dose calculation algorithm for multiple LINACs.

PURPOSE: A photon Monte Carlo (MC) model was commissioned for flattened (FF) and flattening filter free (FFF) 6 MV beam energy. The accuracy of this model, as a single model to be used for three beam matched LINACs, was evaluated. METHODS: Multiple models were created in RayStation v.10A for three linacs equipped with Elekta "Agility" collimator. A clinically commissioned collapsed cone (CC) algorithm (GoldCC), a MC model automatically created from the CC algorithm without further optimization (CCtoMC) and an optimized MC model (GoldMC) were compared with measurements. The validation of the model was performed by following the recommendations of IAEA TRS 430 and comprised of basic validation in a water tank, validation in a heterogeneous phantom and validation of complex IMRT/VMAT paradigms using gamma analysis of calculated and measured dose maps in a 2D-Array. RESULTS: Dose calculation with the GoldMC model resulted in a confidence level of 3% for point measurements in water tank and heterogeneous phantom for measurements performed in all three linacs. The same confidence level resulted for GoldCC model. Dose maps presented an agreement for all models on par to each other with γ criteria 2%/2mm. CONCLUSIONS: The GoldMC model showed a good agreement with measured data and is determined to be accurate for clinical use for all three linacs in this study.

Exploring the aggregation of amyloid-ß 42 through Monte Carlo simulations.

Coarse-grained Monte Carlo simulations are performed for a disordered protein, amyloid-ß 42 to identify the interactions and understand the mechanism of its aggregation. A statistical potential is developed from a selected dataset of intrinsically disordered proteins, which accounts for the respective contributions of the bonded and non-bonded potentials. While, the bonded potential comprises the bond, bend, and dihedral constraints, the nonbonded interactions include van der Waals interactions, hydrogen bonds, and the two-body potential. The two-body potential captures the features of both hydrophobic and electrostatic interactions that brings the chains at a contact distance, while the repulsive van der Waals interactions prevent them from a collapse. Increased two-body hydrophobic interactions facilitate the formation of amorphous aggregates rather than the fibrillar ones. The formation of aggregates is validated from the interchain distances, and the total energy of the system. The aggregate is structurally characterized by the root-mean-square deviation, root-mean-square fluctuation and the radius of gyration. The aggregates are characterized by a decrease in SASA, an increase in the non-local interactions and a distinct free energy minimum relative to that of the monomeric state of amyloid-ß 42. The hydrophobic residues help in nucleation, while the charged residues help in oligomerization and aggregation.

Evaluation of cost-effective system designs for long axial field-of-view PET scanners.

Objective. Current commercial positron emission tomography (PET) scanners have excellent performance and diagnostic image quality primarily due to improvements in scanner sensitivity and time-of-flight (TOF) resolution. Recent years have seen the development of total-body PET scanners with longer axial field-of-view (AFOV) that increase sensitivity for single organ imaging, and also image more of the patient in a single bed position thereby enabling multi-organ dynamic imaging. While studies have shown significant capabilities of these systems, cost will be a major factor in their widespread adoption in the clinic. Here we evaluate alternative designs that achieve many advantages of long AFOV PET while utilizing cost-effective detector hardware.Approach. We utilize Monte Carlo simulations and clinically relevant lesion detectability metric to study the impact of scintillator type lutetium oxyorthosilicate or bismuth germanate (LSO or BGO), scintillator thickness (10-20 mm), and TOF resolution on resultant image quality in a 72 cm long scanner. Detector TOF resolution was varied based on current scanner performance, as well as expected future performance from detector designs that currently hold most promise for scaling into a scanner.Main results. Results indicate that BGO is competitive with LSO (both 20 mm thick) if we assume that it uses TOF (e.g. Cerenkov timing with 450 ps fwhm and Lorentzian distribution) and the LSO scanner has TOF resolution similar to the latest PMT-based scanners (∼500-650 ps). Alternatively, a system using 10 mm thick LSO with 150 ps TOF resolution can also provide similar performance. Both these alternative systems can provide cost savings (25%-33%) relative to a scanner using 20 mm LSO with ∼50% of effective sensitivity, but still 500%-700% higher than a conventional AFOV scanner.Significance. Our results have relevance to the development of long AFOV PET, where reduced cost of these alternative designs can provide wider accessibility for use in situations requiring imaging of multiple organs simultaneously.