keV Μm Research Articles

A deep-learning-based surrogate model for Monte-Carlo simulations of the linear energy transfer in primary brain tumor patients treated with proton-beam radiotherapy

Objective. This study explores the use of neural networks (NNs) as surrogate models for Monte-Carlo (MC) simulations in predicting the dose-averaged linear energy transfer (LET d ) of protons in proton-beam therapy based on the planned dose distribution and patient anatomy in the form of computed tomography (CT) images. As LET d is associated with variability in the relative biological effectiveness (RBE) of protons, we also evaluate the implications of using NN predictions for normal tissue complication probability (NTCP) models within a variable-RBE context. Approach. The predictive performance of three-dimensional NN architectures was evaluated using five-fold cross-validation on a cohort of brain tumor patients (n = 151). The best-performing model was identified and externally validated on patients from a different center (n = 107). LET d predictions were compared to MC-simulated results in clinically relevant regions of interest. We assessed the impact on NTCP models by leveraging LET d predictions to derive RBE-weighted doses, using the Wedenberg RBE model. Main results. We found NNs based solely on the planned dose distribution, i.e. without additional usage of CT images, can approximate MC-based LET d distributions. Root mean squared errors (RMSE) for the median LET d within the brain, brainstem, CTV, chiasm, lacrimal glands (ipsilateral/contralateral) and optic nerves (ipsilateral/contralateral) were 0.36, 0.87, 0.31, 0.73, 0.68, 1.04, 0.69 and 1.24 keV µm−1, respectively. Although model predictions showed statistically significant differences from MC outputs, these did not result in substantial changes in NTCP predictions, with RMSEs of at most 3.2 percentage points. Significance. The ability of NNs to predict LET d based solely on planned dose distributions suggests a viable alternative to compute-intensive MC simulations in a variable-RBE setting. This is particularly useful in scenarios where MC simulation data are unavailable, facilitating resource-constrained proton therapy treatment planning, retrospective patient data analysis and further investigations on the variability of proton RBE.

Particle tracking, recognition and LET evaluation of out-of-field proton therapy delivered to a phantom with implants

Objective. This study aims to assess the composition of scattered particles generated in proton therapy for tumors situated proximal to some titanium (Ti) dental implants. The investigation involves decomposing the mixed field and recording Linear Energy Transfer (LET) spectra to quantify the influence of metallic dental inserts located behind the tumor. Approach. A therapeutic conformal proton beam was used to deliver the treatment plan to an anthropomorphic head phantom with two types of implants inserted in the target volume (made of Ti and plastic, respectively). The scattered radiation resulted during the irradiation was detected by a hybrid semiconductor pixel detector MiniPIX Timepix3 that was placed distal to the Spread-out Bragg peak. Visualization and field decomposition of stray radiation were generated using algorithms trained in particle recognition based on artificial intelligence neural networks (AI NN). Spectral sensitive aspects of the scattered radiation were collected using two angular positions of the detector relative to the beam direction: 0° and 60°. Results. Using AI NN, 3 classes of particles were identified: protons, electrons & photons, and ions & fast neutrons. Placing a Ti implant in the beam’s path resulted in predominantly electrons and photons, contributing 52.2% of the total number of detected particles, whereas for plastic implants, the contribution was 65.4%. Scattered protons comprised 45.5% and 31.9% with and without metal inserts, respectively. The LET spectra were derived for each group of particles identified, with values ranging from 0.01 to 7.5 keV μm−1 for Ti implants/plastic implants. The low-LET component was primarily composed of electrons and photons, while the high-LET component corresponded to protons and ions. Significance. This method, complemented by directional maps, holds the potential for evaluating and validating treatment plans involving stray radiation near organs at risk, offering precise discrimination of the mixed field, and enhancing in this way the LET calculation.

Towards precise LET measurements based on energy deposition of therapeutic ions in Timepix3 detectors

Objective. There is an increasing interest in calculating and measuring linear energy transfer (LET) spectra in particle therapy in order to assess their impact in biological terms. As such, the accuracy of the particle fluence energy spectra becomes paramount. This study focuses on quantifying energy depositions of distinct proton, helium, carbon, and oxygen ion beams using a silicon pixel detector developed at CERN to determine LET spectra in silicon. Approach. While detection systems have been investigated in this pursuit, the scarcity of detectors capable of providing per-ion data with high spatial and temporal resolution remains an issue. This gap is where silicon pixel detector technology steps in, enabling online tracking of single-ion energy deposition. The used detector consisted of a 300 µm thick silicon sensor operated in partial depletion. Main results. During post-processing, artifacts in the acquired signals were identified and methods for their corrections were developed. Subsequently, a correlation between measured and Monte Carlo-based simulated energy deposition distributions was performed, relying on a two-step recalibration approach based on linear and saturating exponential models. Despite the observed saturation effects, deviations were confined below 7% across the entire investigated range of track-averaged LET values in silicon from 0.77 keV µm−1 to 93.16 keV µm−1. Significance. Simulated and measured mean energy depositions were found to be aligned within 7%, after applying artifact corrections. This extends the range of accessible LET spectra in silicon to clinically relevant values and validates the accuracy and reliability of the measurements. These findings pave the way towards LET-based dosimetry through an approach to translate these measurements to LET spectra in water. This will be addressed in a future study, extending functionality of treatment planning systems into clinical routine, with the potential of providing ion-beam therapy of utmost precision to cancer patients.

Comparison of linear energy transfer measurement for therapeutic carbon beam using CR-39 and TLD

The measurement of linear energy transfer (LET) is crucial for the evaluation of the radiation effect in heavy ion therapy. As two detectors which are convenient to implant into the phantom, the performance of CR-39 and thermoluminescence detector (TLD) for LET measurement was compared by experiment and simulation in this study. The results confirmed the applicability of both detectors for LET measurements, but also revealed that the CR-39 detector would lead to potential overestimation of dose-averaged LET compared with the simulation by PHITS, while the TLD would have a large uncertainty measuring ions with LET larger than 20 keV μm−1. The results of this study were expected to improve the detection method of LET for therapeutic carbon beam and would finally be benefit to the quality assurance of heavy ion radiotherapy.

Deep learning-based synthetic dose-weighted LET map generation for intensity modulated proton therapy

The advantage of proton therapy as compared to photon therapy stems from the Bragg peak effect, which allows protons to deposit most of their energy directly at the tumor while sparing healthy tissue. However, even with such benefits, proton therapy does present certain challenges. The biological effectiveness differences between protons and photons are not fully incorporated into clinical treatment planning processes. In current clinical practice, the relative biological effectiveness (RBE) between protons and photons is set as constant 1.1. Numerous studies have suggested that the RBE of protons can exhibit significant variability. Given these findings, there is a substantial interest in refining proton therapy treatment planning to better account for the variable RBE. Dose-average linear energy transfer (LETd) is a key physical parameter for evaluating the RBE of proton therapy and aids in optimizing proton treatment plans. Calculating precise LETd distributions necessitates the use of intricate physical models and the execution of specialized Monte-Carlo simulation software, which is a computationally intensive and time-consuming progress. In response to these challenges, we propose a deep learning based framework designed to predict the LETd distribution map using the dose distribution map. This approach aims to simplify the process and increase the speed of LETd map generation in clinical settings. The proposed CycleGAN model has demonstrated superior performance over other GAN-based models. The mean absolute error (MAE), peak signal-to-noise ratio and normalized cross correlation of the LETd maps generated by the proposed method are 0.096 ± 0.019 keV μm−1, 24.203 ± 2.683 dB, and 0.997 ± 0.002, respectively. The MAE of the proposed method in the clinical target volume, bladder, and rectum are 0.193 ± 0.103, 0.277 ± 0.112, and 0.211 ± 0.086 keV μm−1, respectively. The proposed framework has demonstrated the feasibility of generating synthetic LETd maps from dose maps and has the potential to improve proton therapy planning by providing accurate LETd information.

Genomic view of heavy-ion-induced deletions associated with distribution of essential genes in Arabidopsis thaliana.

Heavy-ion beam, a type of ionizing radiation, has been applied to plant breeding as a powerful mutagen and is a promising tool to induce large deletions and chromosomal rearrangements. The effectiveness of heavy-ion irradiation can be explained by linear energy transfer (LET; keV µm-1). Heavy-ion beams with different LET values induce different types and sizes of mutations. It has been suggested that deletion size increases with increasing LET value, and complex chromosomal rearrangements are induced in higher LET radiations. In this study, we mapped heavy-ion beam-induced deletions detected in Arabidopsis mutants to its genome. We revealed that deletion sizes were similar between different LETs (100 to 290 keV μm-1), that their upper limit was affected by the distribution of essential genes, and that the detected chromosomal rearrangements avoid disrupting the essential genes. We also focused on tandemly arrayed genes (TAGs), where two or more homologous genes are adjacent to one another in the genome. Our results suggested that 100 keV µm-1 of LET is enough to disrupt TAGs and that the distribution of essential genes strongly affects the heritability of mutations overlapping them. Our results provide a genomic view of large deletion inductions in the Arabidopsis genome.

Variable RBE in proton radiotherapy: a comparative study with the predictive Mayo Clinic Florida microdosimetric kinetic model and phenomenological models of cell survival

Objectives. (1) To examine to what extent the cell- and exposure- specific information neglected in the phenomenological proton relative biological effectiveness (RBE) models could influence the computed RBE in proton therapy. (2) To explore similarities and differences in the formalism and the results between the linear energy transfer (LET)-based phenomenological proton RBE models and the microdosimetry-based Mayo Clinic Florida microdosimetric kinetic model (MCF MKM). (3) To investigate how the relationship between the RBE and the dose-mean proton LET is affected by the proton energy spectrum and the secondary fragments. Approach. We systematically compared six selected phenomenological proton RBE models with the MCF MKM in track-segment simulations, monoenergetic proton beams in a water phantom, and two spread-out Bragg peaks. A representative comparison with in vitro data for human glioblastoma cells (U87 cell line) is also included. Main results. Marked differences were observed between the results of the phenomenological proton RBE models, as reported in previous studies. The dispersion of these models’ results was found to be comparable to the spread in the MCF MKM results obtained by varying the cell-specific parameters neglected in the phenomenological models. Furthermore, while single cell-specific correlation between RBE and the dose-mean proton LET seems reasonable above 2 keV μm−1, caution is necessary at lower LET values due to the relevant contribution of secondary fragments. The comparison with in vitro data demonstrates comparable agreement between the MCF MKM predictions and the results of the phenomenological models. Significance. The study highlights the importance of considering cell-specific characteristics and detailed radiation quality information for accurate RBE calculations in proton therapy. Furthermore, these results provide confidence in the use of the MCF MKM for clonogenic survival RBE calculations in proton therapy, offering a more mechanistic approach compared to phenomenological models.

Single proton LET characterization with the Timepix detector and artificial intelligence for advanced proton therapy treatment planning

Objective. Protons have advantageous dose distributions and are increasingly used in cancer therapy. At the depth of the Bragg peak range, protons produce a mixed radiation field consisting of low- and high-linear energy transfer (LET) components, the latter of which is characterized by an increased ionization density on the microscopic scale associated with increased biological effectiveness. Prediction of the yield and LET of primary and secondary charged particles at a certain depth in the patient is performed by Monte Carlo simulations but is difficult to verify experimentally. Approach. Here, the results of measurements performed with Timepix detector in the mixed radiation field produced by a therapeutic proton beam in water are presented and compared to Monte Carlo simulations. The unique capability of the detector to perform high-resolution single particle tracking and identification enhanced by artificial intelligence allowed to resolve the particle type and measure the deposited energy of each particle comprising the mixed radiation field. Based on the collected data, biologically important physics parameters, the LET of single protons and dose-averaged LET, were computed. Main results. An accuracy over 95% was achieved for proton recognition with a developed neural network model. For recognized protons, the measured LET spectra generally agree with the results of Monte Carlo simulations. The mean difference between dose-averaged LET values obtained from measurements and simulations is 17%. We observed a broad spectrum of LET values ranging from a fraction of keV μm−1 to about 10 keV μm−1 for most of the measurements performed in the mixed radiation fields. Significance. It has been demonstrated that the introduced measurement method provides experimental data for validation of LETD or LET spectra in any treatment planning system. The simplicity and accessibility of the presented methodology make it easy to be translated into a clinical routine in any proton therapy facility.

Biophysical characterization of collimated and uncollimated fields in pencil beam scanning proton therapy

Objective. The lateral dose fall-off in proton pencil beam scanning (PBS) technique remains the preferred choice for sparing adjacent organs at risk as opposed to the distal edge due to the proton range uncertainties and potentially high relative biological effectiveness. However, because of the substantial spot size along with the scattering in the air and in the patient, the lateral penumbra in PBS can be degraded. Combining PBS with an aperture can result in a sharper dose fall-off, particularly for shallow targets. Approach. The aim of this work was to characterize the radiation fields produced by collimated and uncollimated 100 and 140 MeV proton beams, using Monte Carlo simulations and measurements with a MiniPIX-Timepix detector. The dose and the linear energy transfer (LET) were then coupled with published in silico biophysical models to elucidate the potential biological effects of collimated and uncollimated fields. Main results. Combining an aperture with PBS reduced the absorbed dose in the lateral fall-off and out-of-field by 60%. However, the results also showed that the absolute frequency-averaged LET (LETF) values increased by a maximum of 3.5 keV μm−1 in collimated relative to uncollimated fields, while the dose-averaged LET (LETD) increased by a maximum of 7 keV μm−1. Despite the higher LET values produced by collimated fields, the predicted DNA damage yields remained lower, owing to the large dose reduction. Significance. This work demonstrated the dosimetric advantages of combining an aperture with PBS coupled with lower DNA damage induction. A methodology for calculating dose in water derived from measurements with a silicon-based detector was also presented. This work is the first to demonstrate experimentally the increase in LET caused by combining PBS with aperture, and to assess the potential DNA damage which is the initial step in the cascade of events leading to the majority of radiation-induced biological effects.

Clinical dose assessment for scanned carbon-ion radiotherapy using linear energy transfer measurements and Monte Carlo simulations

Objective. Dosimetric commissioning of treatment planning systems (TPS) focuses on validating the agreement of the physical dose with experimental data. For carbon-ion radiotherapy, the commissioning of the relative biological effectiveness (RBE) is necessary to predict the clinical outcome based on the radiation quality of the mixed radiation field. In this study, we proposed a approach for RBE commissioning using Monte Carlo (MC) simulations, which was further strengthen by RBE validation based on linear energy transfer (LET) measurements. Approach. First, we tuned the MC simulation based on the results of dosimetric experiments including the beam ranges, beam sizes, and MU calibrations. Furthermore, we compared simulated results to measured depth- and radial-LET distributions of the 430 MeV u−1 carbon-ion spot beam with a 1.5 mm2, 36 μm thick silicon detector. The measured dose-averaged LET (LETd) and RBE were compared with the simulated results. The RBE was calculated based on the mixed beam model with linear-quadratic parameters depending on the LET. Finally, TPS-calculated clinical dose profiles were validated through the tuned MC-based calculations. Main results. A 10 keV μm−1 and 0.15 agreement for LETd and RBE, respectively, were found between simulation and measurement results obtained for a 2σ lateral size of 430 MeV u−1 carbon-ion spot beam in water. These results suggested that the tuned MC simulation can be used with acceptable precision for the RBE and LET calculations of carbon-ion spot beam within the clinical energy range. For physical and clinical doses, the TPS- and MC-based calculations showed good agreements within 1.0% at the centre of the spread-out Bragg peaks. Significance. The tuned MC simulation can accurately reproduce the actual carbon-ion beams, and it can be used to validate the physical and clinical dose distributions calculated by TPS. Moreover, the MC simulation can be used for dosimetric commissioning, including clinical doses, without LET measurements.

Application of a simple DNA damage model developed for electrons to proton irradiation

Proton beam therapy allows irradiating tumor volumes with reduced side effects on normal tissues with respect to conventional x-ray radiotherapy. Biological effects such as cell killing after proton beam irradiations depend on the proton kinetic energy, which is intrinsically related to early DNA damage induction. As such, DNA damage estimation based on Monte Carlo simulations is a research topic of worldwide interest. Such simulation is a mean of investigating the mechanisms of DNA strand break formations. However, past modellings considering chemical processes and DNA structures require long calculation times. Particle and heavy ion transport system (PHITS) is one of the general-purpose Monte Carlo codes that can simulate track structure of protons, meanwhile cannot handle radical dynamics simulation in liquid water. It also includes a simple model enabling the efficient estimation of DNA damage yields only from the spatial distribution of ionizations and excitations without DNA geometry, which was originally developed for electron track-structure simulations. In this study, we investigated the potential application of the model to protons without any modification. The yields of single-strand breaks, double-strand breaks (DSBs) and the complex DSBs were assessed as functions of the proton kinetic energy. The PHITS-based estimation showed that the DSB yields increased as the linear energy transfer (LET) increased, and reproduced the experimental and simulated yields of various DNA damage types induced by protons with LET up to about 30 keV μm−1. These results suggest that the current DNA damage model implemented in PHITS is sufficient for estimating DNA lesion yields induced after protons irradiation except at very low energies (below 1 MeV). This model contributes to evaluating early biological impacts in radiation therapy.

First experimental measurements of 2D microdosimetry maps in proton therapy.

Empirical data in proton therapy indicate that relative biological effectiveness (RBE) is not constant, and it is directly related to the linear energy transfer (LET). The experimental assessment of LET with high resolution would be a powerful tool for minimizing the LET hot spots in intensity-modulated proton therapy, RBE- or LET-guided evaluation and optimization to achieve biologically optimized proton plans, verifying the theoretical predictions of variable proton RBE models, and so on. This could impact clinical outcomes by reducing toxicities in organs at risk. The present work shows the first 2D LET maps obtained at a proton therapy facility using the double scattering delivery mode in clinical conditions by means of new silicon 3D-cylindrical microdetectors. The device consists of a matrix of 121 independent silicon-based detectors that have 3D-cylindrical electrodes of 25-µm diameter and 20-µm depth, resulting each one of them in a well-defined micrometric radiation sensitive volume etched inside the silicon. They have been specifically designed for a hadron therapy, improving the performance of current silicon-based microdosimeters. Microdosimetry spectra were obtained at different positions of the Bragg curve by using a water-equivalent phantom along an 89-MeV pristine proton beam generated in the Y1 proton passive scattering beamline of the Orsay Proton Therapy Centre (Institut Curie, France). Microdosimetry 2D-maps showing the variation of the lineal energy with depth in the three dimensions were obtained in situ during irradiation at clinical fluence rates (∼108 s-1 cm-2 ) for the first time with a spatial resolution of 200µm, the highest achieved in the transverse plane so far. The experimental results were cross-checked with Monte Carlo simulations and a good agreement between the spectra shapes was found. The experimental frequency-mean lineal energy values in silicon were 1.858±0.019keVµm-1 at the entrance, 2.61±0.03keVµm-1 at the proximal distance, 4.97±0.05keVµm-1 close to the Bragg peak, and 8.6±0.1keVµm-1 at the distal edge. They are in good agreement with the expected trends in the literature in clinical proton beams. We present the first 2D microdosimetry maps obtained in situ during irradiation at clinical fluence rates in proton therapy. Our results show that the arrays of 3D-cylindrical microdetectors are a reliable microdosimeter to evaluate LET maps not only in the longitudinal axis of the beam, but also in the transverse plane allowing for LET characterization in three dimensions. This work is a proof of principle showing the capacity of our system to deliver LET 2D maps. This kind of experimental data is needed to validate variable proton RBE models and to optimize LET-guided plans.

The microdosimetic one-hit detector model for calculating the alpha particles detection efficiency of polycarbonate SSNTD

In this work, a polycarbonate (PC) detector with 250μm thickness and 2 cm × 2 cm dimension exposed to alpha particles of an 241Am source is simulated by Geant4 toolkit. Different energies of alpha particles ranging from 0.2 to 5.0 MeV are considered. Spheres with 2μm diameter randomly positioned within the etching layer of the PC are considered as the sensitive volumes. Considering the threshold of 240 keV μm−1 for forming the tracks, specific energy distribution for any alpha energy is computed. Finally, the number of tracks and the detection efficiency are calculated applying the microdosimetric one-hit detector formulation. It is found that the obtained efficiencies differ at most 10% and 12% from the previous simulation and the measurements (adapted with the two different etchings), respectively.

Calculation of alpha particles detection efficiency for polycarbonate detector using Geant4 toolkit

In the present work, alpha particles detection efficiency of polycarbonate detector (PC) is calculated using Geant4 simulation toolkit. A PC detector with 250 µm thickness and dimension of 2.5 × 2.5 cm2 is considered. Assuming the passage of alpha particles of an 241Am source through brass collimators with different lengths, various alpha energies ranging from 0.2 to 2.5 MeV are modeled. Also, an etching layer of 6 µm related to a given electrochemical etching process as well as a threshold stopping power of 240 keV µm-1 required for forming the tracks are used as the input data. The detection efficiency is determined as the ratio of registered tracks per unit area to the number of alpha particles reaching the PC detector surface. To validate the simulation, the results are compared with the experimental data reported in the literature. It is found that the both results agree well within a maximum variation of 10%.

Experimental and Monte Carlo based study of different microdosimetric quantities at mixed radiation environments of nuclear reactor, reprocessing facility and D-D accelerator

Personnel working in the environment of nuclear reactors, fuel reprocessing facilities, high energy accelerators get exposed to a mixed radiation field of electrons, photons, and neutrons with a wide range of energy and fluence. Performance evaluations of the passive personnel dosimeters in these mixed radiation environments require knowledge of the external dose contributed by different types of radiations having variation in their energy, etc. Tissue Equivalent Proportional Counters (TEPC), sensitive to a wide range of particles and energy (Linear Energy Transfer (LET) ∼ 0–1500 keV μm−1), is one of the standard reference detectors which can be used for the characterization of mixed radiation fields. In the present work, HAWK TEPC was used to evaluate different microdosimetric parameters such as dose mean lineal energy (ӯD), average quality factor (Ǭ), the contribution of gamma and neutron dose equivalents for the characterizations of the radiation fields inside in a nuclear research reactor hall, fuel reprocessing facility, D-D neutron beam facility, radionuclide source based neutron calibration facilities, etc. Measured results were compared with Monte Carlo simulated values using radiation transport code PHITS. TEPC measurement shows that the radiation work field around nuclear reactor facility is dominated by gamma rays with approximate ӯD and Ǭ of ∼42 keV μm−1 and ∼2.3, respectively. Whereas ӯD and Ǭ values for neutron dominated locations in fuel reprocessing facility are 82 keV μm−1 and 8.2, respectively. Ǭ values near thermal neutron beam line in research reactor are close to 1 due to (n, γ) reaction. For 2.54 MeV mono-energetic D-D neutron beam, measured and calculated dose mean lineal energies (ӯD) are 52.5 keV μm−1 and 59.6 keV μm−1, respectively, whereas corresponding Ǭ values are 11.23 and 13.01. Obtained values can be utilized to correlate the neutron and gamma dose equivalents measured by passive detectors used for personnel monitoring program to enhance confidence in the dosimetry.

On the calculation of the relative biological effectiveness of ion radiation therapy using a biological weighting function, the microdosimetric kinetic model (MKM) and subsequent corrections (non-Poisson MKM and modified MKM)

Objective. To investigate similarities and differences in the formalism, processing, and the results of relative biological effectiveness (RBE) calculations with a biological weighting function (BWF), the microdosimetric kinetic model (MKM) and subsequent modifications (non-Poisson MKM, modified MKM). This includes: (a) the extension of the V79-RBE10% BWF to model the RBE for other clonogenic survival levels; (b) a novel implementation of MKMs as weighting functions; (c) a benchmark against Chinese Hamster lung fibroblast (V79) in vitro data; (d) a study on the effect of pre- or post- processing the average biophysical quantities used for the RBE calculations; (e) a possible modification of the modified MKM parameters to improve the model accuracy at high linear energy transfer (LET). Methodology. Lineal energy spectra were simulated for two spherical targets (diameter = 0.464 or 1.0 μm) using PHITS for 1H, 4He, 12C, 20Ne, 40Ar, 56Fe and 132Xe ions. The results of the in silico calculations were compared with published in vitro data. Main results. All models appear to underestimate the RBE α of hydrogen ions. All MKMs generally overestimate the RBE50%, RBE10% and RBE1% for ions with an LET greater than ∼200 keV μm−1. This overestimation is greater for small surviving fractions and is likely due to the assumption of a radiation-independent quadratic term of clonogenic survival (ß). The overall RBE trends seem to be best described by the novel ‘post-processing average’ implementation of the non-Poisson MKM. In case of calculations with the non-Poisson MKM, pre- or post- processing the average biophysical quantities affects the computed RBE values significantly. Significance. This study presents a systematic analysis of the formalism and results of widely used microdosimetric models of clonogenic survival for ions relevant for cancer particle therapy and space radiation protection. Points for improvements were highlighted and will contribute to the development of upgraded biophysical models.

GEANT4 modeling of alpha particles detection efficiency for polycarbonate SSNTD

In this paper, a simple model based on the GEANT4 simulation toolkit is presented to determine the detection efficiency of polycarbonate detectors. Different alpha energies obtained by traveling of 241Am source alpha particles through different air columns are considered. Experimental measurement are also carried out using two different solutions and electrochemical etching conditions. The thicknesses of the bulk and the electrochemical etching layers extracted from the etching process are assumed as the key parameters. When the vertical range of an alpha particle corresponding to a linear energy transfer above the threshold required for forming the tracks lies between the two above thicknesses, the model counts a track. Fitting the modeled and the experimental efficiencies in the energy range 0.2 to 5 MeV shows that the threshold for forming the tracks in polycarbonate is 240 keV μm−1. Finally, the linear energy transfer threshold as well as the model for track formation presented here can be utilized for future investigations on the polycarbonate detector.

Neuroprotective Effects of Ultra-High Dose Rate FLASH Bragg Peak Proton Irradiation

To investigate brain tissue response to ultra-high dose rate (uHDR, FLASH) and standard dose rate (SDR) proton irradiations in the Bragg peak region. Active scanning uHDR delivery was established for proton beams for investigation of dose rate effects between clinical SDR and uHDR at ∼10 Gy in the Bragg peak region (dose-averaged linear energy transfer [LETD] ranging from 4.5 to 10.2 keV µm-1 ). Radiation- induced injury of neuronal tissue was assessed by studying the DNA double strand break repair kinetics surrogated by nuclear γH2AX staining (radiation induced foci [RIF]), microvascular density and structural integrity (MVD, CD31+ endothelium), and inflammatory microenvironmental response (CD68+ microglia/macrophages and high mobility group box protein 1[HMGB]) in healthy C57BL/6 mouse brains. Averaged dose rates achieved were 0.17 Gy/s (SDR) and 120 Gy/s (uHDR). The fraction of RIF-positive cells increased after SDR ∼10-fold, whereas a significantly lower fraction of RIF-positive cells was found after uHDR versus SDR (∼2 fold, P < .0001). Moreover, uHDR substantially preserved the microvascular architecture and reduced microglia/macrophage regulated associated inflammation as compared with SDR. The feasibility of uHDR raster scanning proton irradiation is demonstrated to elicit FLASH sparing neuroprotective effects compared to SDR in a preclinical in vivo model.

A Monte Carlo study of different LET definitions and calculation parameters for proton beam therapy

The strong in vitro evidence that proton Relative Biological Effectiveness (RBE) varies with Linear Energy Transfer (LET) has led to an interest in applying LET within treatment planning. However, there is a lack of consensus on LET definition, Monte Carlo (MC) parameters or clinical methodology. This work aims to investigate how common variations of LET definition may affect potential clinical applications. MC simulations (GATE/GEANT4) were used to calculate absorbed dose and different types of LET for a simple Spread Out Bragg Peak (SOBP) and for four clinical PBT plans covering a range of tumour sites. Variations in the following LET calculation methods were considered: (i) averaging (dose-averaged LET (LETd) & track-averaged LET); (ii) scoring (LETd to water, to medium and to mass density); (iii) particle inclusion (LETd to all protons, to primary protons and to particles); (iv) MC settings (hit type and Maximum Step Size (MSS)). LET distributions were compared using: qualitative comparison, LET Volume Histograms (LVHs), single value criteria (maximum and mean values) and optimised LET-weighted dose models. Substantial differences were found between LET values in averaging, scoring and particle type. These differences depended on the methodology, but for one patient a difference of ∼100% was observed between the maximum LETd for all particles and maximum LETd for all protons within the brainstem in the high isodose region (4 keV μm−1 and 8 keV μm−1 respectively). An RBE model using LETd including heavier ions was found to predict substantially different LET-weighted dose compared to those using other LET definitions. In conclusion, the selection of LET definition may affect the results of clinical metrics considered in treatment planning and the results of an RBE model. The authors’ advocate for the scoring of dose-averaged LET to water for primary and secondary protons using a random hit type and automated MSS.

Carbon Ion FLASH Dose-Rate Radiotherapy: First Investigation in Human Brain Organoids

<h3>Purpose/Objective(s)</h3> In this work, we investigated and compared the effects of carbon ion ultra-high dose-rate (uHDR; FLASH) vs standard dose-rate (SDR) beams on brain organoids. <h3>Materials/Methods</h3> For testing the effects of high dose-rate radiotherapy using carbon ions, dose rates of ∼40 Gy/s (uHDR) and 0.15 Gy/s (SDR) were applied at 7.4 Gy dose (LETd = 12 keV µm-1). The effectiveness of uHDR vs. SDR carbon ion beams was tested using brain organoids generated from human induced pluripotent stem cells (iPS). Brain organoids were evaluated for their cellular composition and radiation induced DNA-damage repair using immunofluorescence. Quantitative analysis was performed using semi-automated imaging software. <h3>Results</h3> Multicellular brain organoids were characterized by the presence of neuronal progenitor cells (nestin positive), more differentiated neurons (Tuj1 positive), and astrocytic cells (GFAP positive). Following irradiation, the cells were further evaluated for presence of radiation induced DNA double strand breaks surrogated by nuclear γH2AX foci (RIF). While both uHDR and SDR irradiations led to RIF formation, uHDR markedly expressed reduced RIF vs. SDR at 1-hour post-irradiation. <h3>Conclusion</h3> This study indicates feasibility of brain organoids as a healthy brain tissue model for studying the effects of particle radiation. Here reported encouraging normal tissue sparing effect of uHDR carbon ion beams, at relevant plateau LET level, warrants further investigation of carbon ion FLASH radiotherapy.