Dose Maps Research Articles

Global dose distributions of neutrons and gamma-rays on the Moon

Dose assessment on the lunar surface is important for future long-term crewed activity. In addition to the major radiation of energetic charged particles from galactic cosmic rays (GCRs), neutrons and gamma-rays are generated by nuclear interactions of space radiation with the Moon’s surface materials, as well as natural radioactive nuclides. We obtained neutron and gamma-ray ambient dose distributions on the Moon using Geant4 Monte Carlo simulations combined with the Kaguya gamma-ray spectrometer measurement dataset from February 10 to May 28, 2009. The neutron and gamma-ray dose rates varied in the ranges of 58.7–71.5 mSv/year and 3.33–3.76 mSv/year, respectively, depending on the lunar geological features. The lunar neutron dose was high in the basalt-rich mare, where the iron- and titanium-rich regions are present, due to their large average atomic mass. As expected, the lunar gamma-ray dose map was similar to the distribution of natural radioactive elements (238U, 232Th, and 40K), although the GCR-induced secondary gamma-ray dose was significant at ~ 3.4 mSv/year. The lunar secondary dose contribution resulted in an additional dose of 12–15% to the primary GCR particles. Global dose distributions on the lunar surface will help identify better locations for long-term stays and suggest radiation protection strategies for future crewed missions.

Analysis of the Ground Level Enhancement GLE 60 on 15 April 2001, and Its Space Weather Effects: Comparison With Dosimetric Measurements

AbstractAs a result of notable solar activity observed in April 2001, one of the strongest ground level enhancements (GLE) of solar cycle 23 occurred, namely GLE # 60 on 15 April 2001. In this paper, we derived the spectral and angular characteristics, and apparent source position of the solar protons during the GLE # 60, using a verified by direct measurements model and employing the calibrated neutron monitor yield function. Subsequently, employing the updated and verified by balloon measurements dosimetric model: Oulu CRAC:DOMO (Cosmic Ray Atmospheric Cascade: Dosimetric Model) we computed the dose rates throughout the event at several altitudes using the obtained spectra as an input. A global map of the ambient dose at an altitude of 35 kft is computed. A comparison with direct dosimetric measurements obtained by a Liulin device during an intercontinental flight is performed and good agreement is achieved.

Fast dose calculation in x-ray guided interventions by using deep learning

Objective. Patient dose estimation in x-ray-guided interventions is essential to prevent radiation-induced biological side effects. Current dose monitoring systems estimate the skin dose based in dose metrics such as the reference air kerma. However, these approximations do not take into account the exact patient morphology and organs composition. Furthermore, accurate organ dose estimation has not been proposed for these procedures. Monte Carlo simulation can accurately estimate the dose by recreating the irradiation process generated during the x-ray imaging, but at a high computation time, limiting an intra-operative application. This work presents a fast deep convolutional neural network trained with MC simulations for patient dose estimation during x-ray-guided interventions. Approach. We introduced a modified 3D U-Net that utilizes a patient’s CT scan and the numerical values of imaging settings as input to produce a Monte Carlo dose map. To create a dataset of dose maps, we simulated the x-ray irradiation process for the abdominal region using a publicly available dataset of 82 patient CT scans. The simulation involved varying the angulation, position, and tube voltage of the x-ray source for each scan. We additionally conducted a clinical study during endovascular abdominal aortic repairs to validate the reliability of our Monte Carlo simulation dose maps. Dose measurements were taken at four specific anatomical points on the skin and compared to the corresponding simulated doses. The proposed network was trained using a 4-fold cross-validation approach with 65 patients, and evaluating the performance on the remaining 17 patients during testing. Main results. The clinical validation demonstrated a average error within the anatomical points of 5.1%. The network yielded test errors of 11.5 ± 4.6% and 6.2 ± 1.5% for peak and average skin doses, respectively. Furthermore, the mean errors for the abdominal region and pancreas doses were 5.0 ± 1.4% and 13.1 ± 2.7%, respectively. Significance. Our network can accurately predict a personalized 3D dose map considering the current imaging settings. A short computation time was achieved, making our approach a potential solution for dose monitoring and reporting commercial systems.

DOSE MAP BLOOD IRRADIATOR DOSIMETRY SYSTEM: CUSTOMIZED BLOOD EQUIVALENT PHANTOM AND GAFCHROMIC EBT-XD FILM

Blood irradiation is done to sterilize and cease donor T-lymphocytes functionality to avoid Transfusion Associated-Graft Versus Host Disease (TA-GVHD). Dose mapping is the primary means of ensuring that the irradiation process is being conducted correctly. The aim of this study is to measure the minimum, maximum and central absorbed gamma radiation dose delivered at the newly customized blood equivalent phantom using a Gafchromic EBT-XD film. A Gammacell 3000 Elan Blood Irradiator with Cesium-137 source was used. To obtain the dose calibration curves, the films were placed at the center of a phantom and irradiated with the dose range from 5 Gy to 35 Gy with one unirradiated film as a control. The dose calibration curve was plotted using red, green and blue channels. While, for the dose mapping measurement, the irradiation exposure of 9.03 minutes was used to deliver a central dose of 25 Gy to the film. The response of the film will be compared with the GC Dose Mapping Report 2022 which used a water equivalent medium as a phantom. For the calibration curve, the red channel of the film was utilized in this study as it had a higher signal than the other channel. The doses obtained at central, minimum and maximum using EBT-XD film with customized blood equivalent phantom were in agreement with that obtained from GC Dose Mapping Report 2022 to be within ±7.6%, ±8.51% and ±9.95% respectively. We concluded that the customized blood equivalent phantom together with EBT-XD film has a potential to map the dose in blood irradiator accurately. The doses obtained from EBT-XD film were in the range of doses needed to inhibit the proliferation of the T-lymphocytes with central, minimum and maximum doses were 26.9 Gy, 17.2 Gy and 35.7 Gy respectively.

Deep learning proton beam range estimation model for quality assurance based on two-dimensional scintillated light distributions in simulations.

Many studies have utilized optical camera systems with volumetric scintillators for quality assurances (QA) to estimate the proton beam range. However, previous analytically driven range estimation methods have the difficulty to derive the dose distributions from the scintillation images with quenching and optical effects. In this study, a deep learning method utilized to QA was used to predict the beam range and spread-out Bragg peak (SOBP) for two-dimensional (2D) map conversion from the scintillation light distribution (LD) into the dose distribution in a water phantom. The 2D residual U-net modeling for deep learning was used to predict the 2D water dose map from a 2D scintillation LD map. Monte Carlo simulations for dataset preparation were performed with varying monoenergetic proton beam energies, field sizes, and beam axis shifts. The LD was reconstructed using photons backpropagated from the aperture as a virtual lens. The SOBP samples were constructed based on monoenergetic dose distributions. The training set, including the validation set, consisted of 8659 image pairs of LD and water dose maps. After training, dose map prediction was performed using a 300 image pair test set generated under random conditions. The pairs of simulated and predicted dose maps were analyzed by Bragg peak fitting and gamma index maps to evaluate the model prediction. The estimated beam range and SOBP width resolutions were 0.02and 0.19mm respectively for varying beam conditions, and the beam range and SOBP width deviations from the reference simulation result were less than 0.1and 0.8mm respectively. The simulated and predicted distributions showed good agreement in the gamma analysis, except for rare cases with failed gamma indices in the proximal and field-marginal regions. The deep learning conversion method using scintillation LDs in an optical camera system with a scintillator is feasible for estimating proton beam range and SOBP width with high accuracy.

A Transformer-Embedded Multi-Task Model for Dose Distribution Prediction.

Radiation therapy is a fundamental cancer treatment in the clinic. However, to satisfy the clinical requirements, radiologists have to iteratively adjust the radiotherapy plan based on experience, causing it extremely subjective and time-consuming to obtain a clinically acceptable plan. To this end, we introduce a transformer-embedded multi-task dose prediction (TransMTDP) network to automatically predict the dose distribution in radiotherapy. Specifically, to achieve more stable and accurate dose predictions, three highly correlated tasks are included in our TransMTDP network, i.e. a main dose prediction task to provide each pixel with a fine-grained dose value, an auxiliary isodose lines prediction task to produce coarse-grained dose ranges, and an auxiliary gradient prediction task to learn subtle gradient information such as radiation patterns and edges in the dose maps. The three correlated tasks are integrated through a shared encoder, following the multi-task learning strategy. To strengthen the connection of the output layers for different tasks, we further use two additional constraints, i.e. isodose consistency loss and gradient consistency loss, to reinforce the match between the dose distribution features generated by the auxiliary tasks and the main task. Additionally, considering many organs in the human body are symmetrical and the dose maps present abundant global features, we embed the transformer into our framework to capture the long-range dependencies of the dose maps. Evaluated on an in-house rectum cancer dataset and a public head and neck cancer dataset, our method gains superior performance compared with the state-of-the-art ones. Code is available at https://github.com/luuuwen/TransMTDP.

Real-time, acquisition parameter-free voxel-wise patient-specific Monte Carlo dose reconstruction in whole-body CT scanning using deep neural networks

ObjectiveWe propose a deep learning-guided approach to generate voxel-based absorbed dose maps from whole-body CT acquisitions.MethodsThe voxel-wise dose maps corresponding to each source position/angle were calculated using Monte Carlo (MC) simulations considering patient- and scanner-specific characteristics (SP_MC). The dose distribution in a uniform cylinder was computed through MC calculations (SP_uniform). The density map and SP_uniform dose maps were fed into a residual deep neural network (DNN) to predict SP_MC through an image regression task. The whole-body dose maps reconstructed by the DNN and MC were compared in the 11 test cases scanned with two tube voltages through transfer learning with/without tube current modulation (TCM). The voxel-wise and organ-wise dose evaluations, such as mean error (ME, mGy), mean absolute error (MAE, mGy), relative error (RE, %), and relative absolute error (RAE, %), were performed.ResultsThe model performance for the 120 kVp and TCM test set in terms of ME, MAE, RE, and RAE voxel-wise parameters was − 0.0302 ± 0.0244 mGy, 0.0854 ± 0.0279 mGy, − 1.13 ± 1.41%, and 7.17 ± 0.44%, respectively. The organ-wise errors for 120 kVp and TCM scenario averaged over all segmented organs in terms of ME, MAE, RE, and RAE were − 0.144 ± 0.342 mGy, and 0.23 ± 0.28 mGy, − 1.11 ± 2.90%, 2.34 ± 2.03%, respectively.ConclusionOur proposed deep learning model is able to generate voxel-level dose maps from a whole-body CT scan with reasonable accuracy suitable for organ-level absorbed dose estimation.Clinical relevance statementWe proposed a novel method for voxel dose map calculation using deep neural networks. This work is clinically relevant since accurate dose calculation for patients can be carried out within acceptable computational time compared to lengthy Monte Carlo calculations.Key Points• We proposed a deep neural network approach as an alternative to Monte Carlo dose calculation.• Our proposed deep learning model is able to generate voxel-level dose maps from a whole-body CT scan with reasonable accuracy, suitable for organ-level dose estimation.• By generating a dose distribution from a single source position, our model can generate accurate and personalized dose maps for a wide range of acquisition parameters.

Assessment of radioactivity of the Sobirsoy reservoir and adjacent areas of the Nurabad district, Uzbekistan

Abstract Reservoirs and artificial channels are vital sources of water for irrigated areas in arid and semi-arid regions. As such, monitoring the radioecological state of these water bodies is crucial for public health and safety. In this study, we aimed to evaluate the radioecological condition of the Sobirsoy reservoir and surrounding areas. We used gamma-spectrometry to measure natural and anthropogenic radionuclides, and coal sorbents to measure radon flux density on the ground surface. Based on our findings, we conducted a radiological risk assessment and compiled an exposure dose map of the study area. The results showed that the degree of radioactive danger to people in populated areas is within safe limits. Overall, our study provides important information for policymakers and stakeholders to make informed decisions regarding the management and safety of the Sobirsoy reservoir and its surroundings.

Fast D M,M calculation in LDR brachytherapy using deep learning methods

Objective. The Monte Carlo (MC) method provides a complete solution to the tissue heterogeneity effects in low-energy low-dose rate (LDR) brachytherapy. However, long computation times limit the clinical implementation of MC-based treatment planning solutions. This work aims to apply deep learning (DL) methods, specifically a model trained with MC simulations, to predict accurate dose to medium in medium (D M,M) distributions in LDR prostate brachytherapy. Approach. To train the DL model, 2369 single-seed configurations, corresponding to 44 prostate patient plans, were used. These patients underwent LDR brachytherapy treatments in which 125I SelectSeed sources were implanted. For each seed configuration, the patient geometry, the MC dose volume and the single-seed plan volume were used to train a 3D Unet convolutional neural network. Previous knowledge was included in the network as an r 2 kernel related to the first-order dose dependency in brachytherapy. MC and DL dose distributions were compared through the dose maps, isodose lines, and dose-volume histograms. Features enclosed in the model were visualized. Main results. Model features started from the symmetrical kernel and finalized with an anisotropic representation that considered the patient organs and their interfaces, the source position, and the low- and high-dose regions. For a full prostate patient, small differences were seen below the 20% isodose line. When comparing DL-based and MC-based calculations, the predicted CTV D 90 metric had an average difference of −0.1%. Average differences for OARs were −1.3%, 0.07%, and 4.9% for the rectum D 2cc, the bladder D 2cc, and the urethra D 0.1cc. The model took 1.8 ms to predict a complete 3D D M,M volume (1.18 M voxels). Significance. The proposed DL model stands for a simple and fast engine which includes prior physics knowledge of the problem. Such an engine considers the anisotropy of a brachytherapy source and the patient tissue composition.

Dose mapping in Cesium-137 blood irradiator using novel Ge-doped silica optical fibres in comparison with gafchromic EBT-XD film: A preliminary study

The purpose of dose-mapping in blood irradiation is to ensure radiation dose delivered to the blood bags is within the specified limits of minimum 15 Gy and maximum 50 Gy, thus preventing any blood transfusion-related diseases. In the present study, we investigated the feasibility of novel fabricated germanium-doped (Ge-doped) silica-based optical fibre as a potential passive dosimeter for dose-mapping of a Cesium-137 (Cs-137) gamma blood irradiator. The calibration curve of optical fibres and Gafchromic EBT-XD film was established by irradiating the samples in the dose range from 5 to 35 Gy at the central of a water-equivalent phantom in a dedicated blood irradiator machine. For dose mapping, the fabricated optical fibres and EBT-XD film were irradiated for a gamma exposure of 8 min and 56 s to deliver a central dose of 25 Gy. Thermoluminescence measurements were performed using a Harshaw 3500 TLD reader at a maximum acquisition temperature of 400 °C. The results of optical fibres were compared with EBT-XD film. The absorbed dose obtained from optical fibres shows deviation of within 0.1%–10.4% compared to that of dose of the film. The fabricated optical fibre studied in this work have a good potential as a new passive dosimeter for characterisation and dose mapping of gamma blood irradiator due to its high sensitivity and reproducible characteristics.

Voxel based morphometry-detected white matter volume loss after multi-modality treatment in high grade glioma patients.

Radiotherapy (RT) and chemotherapy are components of standard multi-modality treatment of high grade gliomas (HGG) aimed at achieving local tumor control. Treatment is neurotoxic and RT plays an important role in this, inducing damage even distant to the RT target volume. This retrospective longitudinal study evaluated the effect of treatment on white matter and gray matter volume in the tumor-free hemisphere of HGG patients using voxel based morphometry (VBM). 3D T1-weighted MR images of 12 HGG patients at multiple timepoints during standard treatment were analyzed using VBM. Segmentation of white matter and gray matter of the tumor-free hemisphere was performed. Multiple general linear models were used to asses white matter and gray matter volumetric differences between time points. A mean RT dose map was created and compared to the VBM results. Diffuse loss of white matter volume, mainly throughout the frontal and parietal lobe, was found, grossly overlapping regions that received the highest RT dose. Significant loss of white matter was first noticed after three cycles of chemotherapy and persisted after the completion of standard treatment. No significant loss of white matter volume was observed between pre-RT and the first post-RT follow-up timepoint, indicating a delayed effect. This study demonstrated diffuse and early-delayed decreases in white matter volume of the tumor-free hemisphere in HGG patients after standard treatment. White matter volume changes occurred mainly throughout the frontal and parietal lobe and grossly overlapped with areas that received the highest RT dose.

Multi-level progressive transfer learning for cervical cancer dose prediction

Recently, deep learning has accomplished the automation of radiation therapy planning, enhancing its quality and efficiency. However, such progress comes at the cost of a large amount of clinical data. For some low-incidence cancers, i.e., cervical cancer, with limited available data, current data-hungry deep models fail to achieve satisfactory performance. To address this, in this paper, considering that cervical cancer and rectum cancer share the same scanning area and organs at risk (OARs), we resort to transfer learning to transfer the knowledge acquired from rectum cancer (source domain) to cervical cancer (target domain) to perform dose map prediction task. To overcome the possible negative transferring problem, we design a two-phase paradigm to progressively transfer knowledge. In the first phase, we aggregate the data of the two domains by linear interpolation and pre-train an aggregated network with the aggregated data to perceive the target dose distribution beforehand. In the second phase, we elaborately design two modules, i.e., a Feature-level Transfer (FT) Module, and an Image-level Transfer (IT) Module, to selectively transfer knowledge in multi-level. Specifically, the FT module aims to preserve those filters that are more helpful while the IT module tries to highlight those samples with more target-specific knowledge. Extensive experiments proclaim the exemplary performance of our proposed method compared with other state-of-the-art methods.

Influence on voxel-based dosimetry: noise effect on absorbed dose dosimetry at single time-point versus sequential single-photon emission computed tomography.

The purpose of this study was to evaluate how statistical fluctuation in single-photon emission computed tomography (SPECT) images propagate to absorbed dose maps. SPECT/computed tomography (CT) images of iodine-131 filled phantoms, using different acquisition and processing protocols, were evaluated using STRATOS software to assess the absorbed dose distribution at the voxel level. Absorbed dose values and coefficient of variation (COV) were analyzed for dosimetry based on single time-point SPECT images and time-integrated activities of SPECT sequences with low and high counts. Considering dosimetry based on a single time-point, the mean absorbed dose was not significantly affected by total counts or reconstruction parameters, but the uniformity of the absorbed dose maps had an almost linear correlation with SPECT noise. When high- and low-count SPECT sequences were used to generate an absorbed dose map, the absorbed dose COV for each of the temporal sequences was slightly lower than the absorbed dose COV based on the single SPECT image with the highest count included in the sequence. The impact of changes in SPECT counts and reconstruction parameters is almost linear when dosimetry is based on isolated SPECT images, but less pronounced when dosimetry is based on sequential SPECTs.

Evaluation of the cumulative Cherenkov converted dose on TSET patients with multiple Cherenkov cameras.

Cherenkov images can be used for the quality assurance of dose homogeneity in total skin electron therapy (TSET). For the dose mapping purpose, this study reconstructed the patient model from 3D scans using registration algorithms and computer animation techniques. The Cherenkov light emission of the patient's surface was extracted from multi-view Cherenkov images, converted into dose distribution, and projected onto the patient's 3D model, allowing for dose cumulation and evaluation. The projected result from multiple Cherenkov cameras provides additional information about Cherenkov emission on the sides of the patients, which improves the agreement between the Cherenkov converted dose and the OSLD measurements.

The relationship between radiation dose and bevacizumab-related imaging abnormality in patients with brain tumors: A voxel-wise normal tissue complication probability (NTCP) analysis.

Bevacizumab-related imaging abnormality (BRIA), appearing as areas of restricted diffusion on magnetic resonance imaging (MRI) and representing atypical coagulative necrosis pathologically, has been observed in patients with brain tumors receiving radiotherapy and bevacizumab. We investigated the role of cumulative radiation dose in BRIA development in a voxel-wise analysis. Patients (n = 18) with BRIA were identified. All had high-grade gliomas or brain metastases treated with radiotherapy and bevacizumab. Areas of BRIA were segmented semi-automatically on diffusion-weighted MRI with apparent diffusion coefficient (ADC) images. To avoid confounding by possible tumor, hypoperfusion was confirmed with perfusion imaging. ADC images and radiation dose maps were co-registered to a high-resolution T1-weighted MRI and registration accuracy was verified. Voxel-wise normal tissue complication probability analyses were performed using a logistic model analyzing the relationship between cumulative voxel equivalent total dose in 2 Gy fractions (EQD2) and BRIA development at each voxel. Confidence intervals for regression model predictions were estimated with bootstrapping. Among 18 patients, 39 brain tumors were treated. Patients received a median of 4.5 cycles of bevacizumab and 1-4 radiation courses prior to BRIA appearance. Most (64%) treated tumors overlapped with areas of BRIA. The median proportion of each BRIA region of interest volume overlapping with tumor was 98%. We found a dose-dependent association between cumulative voxel EQD2 and the relative probability of BRIA (β0 = -5.1, β1 = 0.03 Gy-1, γ = 1.3). BRIA is likely a radiation dose-dependent phenomenon in patients with brain tumors receiving bevacizumab and radiotherapy. The combination of radiation effects and tumor microenvironmental factors in potentiating BRIA in this population should be further investigated.

GPU-accelerated Monte Carlo simulation of electron and photon interactions for radiotherapy applications

Objective. The Monte Carlo simulation software is a valuable tool in radiation therapy, in particular to achieve the needed accuracy in the dose evaluation for the treatment plans optimisation. The current challenge in this field is the time reduction to open the way to many clinical applications for which the computational time is an issue. In this manuscript we present an innovative GPU-accelerated Monte Carlo software for dose valuation in electron and photon based radiotherapy, developed as an update of the FRED (Fast paRticle thErapy Dose evaluator) software. Approach. The code transports particles through a 3D voxel grid, while scoring their energy deposition along their trajectory. The models of electromagnetic interactions in the energy region between 1 MeV—1 GeV available in literature have been implemented to efficiently run on GPUs, allowing to combine a fast tracking while keeping high accuracy in dose assessment. The FRED software has been bench-marked against state-of-art full MC (FLUKA, GEANT4) in the realm of two different radiotherapy applications: Intra-Operative Radio Therapy and Very High Electron Energy radiotherapy applications. Results. The single pencil beam dose-depth profiles in water as well as the dose map computed on non-homogeneous phantom agree with full-MCs at 2% level, observing a gain in processing time from 200 to 5000. Significance. Such performance allows for computing a plan with electron beams in few minutes with an accuracy of ∼%, demonstrating the FRED potential to be adopted for fast plan re-calculation in photon or electron radiotherapy applications.

Systematic dosimetric evaluation of risk of osteoradionecrosis (DERO): First results of dose reporting for preventing teeth osteoradionecrosis after head and neck irradiation

PurposeOsteoRadioNecrosis (ORN) is a late complication of radiation for head and neck cancer. Predicting ORN is a major challenge. We developed DERO (Dosimetric Evaluation of Risk of ORN), a semi-automatic tool which reports doses delivered to tooth-bearing sectors, to guide post-therapeutic dental care. We present the method and the first results of a 125-patient prospective cohort. Material and methodsDosimetric data of patients treated with IMRT for head and neck cancer were prospectively segmented to the DERO algorithm. Four arches corresponding to 8-tooth sectors were semi-automatically generated. Thirty-two cylindrical Regions Of Interest (ROI) corresponding to each tooth and surrounding periodontium were created by linear interpolation. Mean doses (Dmean) of ROI were extracted and included in a database, along with data about primary tumor site, laterality and dose values from organs at risk. Dmean to tooth sectors were computed for molar sectors, (teeth X5 to X8) and anterior sectors (teeth X1 to X4). An individual dose map was generated and delivered to patients and dentists. ResultsDosimetric data from 125 patients treated with Tomotherapy® were prospectively collected and analyzed: 9 parotid tumors (PA), 41 Sub-Hyoid tumors (larynx, hypopharynx) (SH), 43 Oropharynx tumors (OR), 32 Oral Cavity tumors (OC). Irradiation was unilateral for 100% of PA tumors (9), 12% of OR tumors (5) and 47% of OC tumors (15). For unilateral cervical irradiation, Dmean in ipsilateral molar sectors was 54Gy for OC tumors, 45Gy for OR tumors, 20Gy for PA tumors. For Oral Cavity bilateral irradiation, Dmean was high in all tooth sectors, 49 to 55Gy. For SH tumors, Dmean in molar sectors was 27Gy. A dose gradient of 10 to 20Gy was observed between molar and anterior sectors whether radiation was uni or bilateral. ConclusionMandibular molar sectors of Oropharynx and Oral Cavity tumors were exposed to high Dmean of 40 to 50Gy. On the other hand, tooth sectors received lower doses for SH radiation. The DERO tool guide post-radiation dental care with a personalized dosimetric cartography to patient. With data update and patient follow-up, we will be able to determine ORN risk after head and neck radiation.

Modelling of the electron range for use of E-beam treatment for boned dry-cured hams sanitation

The heterogeneity of the dry-cured ham size and the fat layer thickness can compromise the electron range throughout the piece and, therefore, the E-beam treatment effectiveness to guarantee its microbiological and sensory quality. The present work aimed to model the electron range in boned dry-cured ham according to the mentioned factors. For that, a dose mapping was carried out applying 2 kGy in experimental blocks manufactured with different thicknesses of lard and/or lean ham. The model, that explains 99.2% of the dose distribution variability, consisted in both a two-phase linear and a modified Gompertz functions. According to the model, the thicker the subcutaneous fat layer, the greater the electron range. The model estimated that the two-face treatment at 2 kGy on hams with a 10-mm external fat layer would be sufficient to sanitize the area of highest risk of contamination during deboning process. Industrial relevanceThe export of deboned dry-cured ham to countries such as the USA and China requires the absence of Listeria monocytogenes in 25 g of product. The E-beam treatment at doses between 2 and 3 kGy provides to achieve this objective without product quality losses. The model developed in this work allows to predict the absorbed dose according to the piece size and the thickness of the subcutaneous fat layer. The results show that the distribution of the absorbed dose after bilateral treatment with 2 kGy is more uniform when the thickness of the piece is approximately 80 mm and the thickness of the fat layer is 10–30 mm. The model also makes it possible to quantify the optimal thickness of the specimen when the subcutaneous fat layer is <10 mm or >30 mm.

Dose accumulation for MR-guided adaptive radiotherapy: From practical considerations to state-of-the-art clinical implementation.

MRI-linear accelerator (MR-linac) devices have been introduced into clinical practice in recent years and have enabled MR-guided adaptive radiation therapy (MRgART). However, by accounting for anatomical changes throughout radiation therapy (RT) and delivering different treatment plans at each fraction, adaptive radiation therapy (ART) highlights several challenges in terms of calculating the total delivered dose. Dose accumulation strategies-which typically involve deformable image registration between planning images, deformable dose mapping, and voxel-wise dose summation-can be employed for ART to estimate the delivered dose. In MRgART, plan adaptation on MRI instead of CT necessitates additional considerations in the dose accumulation process because MRI pixel values do not contain the quantitative information used for dose calculation. In this review, we discuss considerations for dose accumulation specific to MRgART and in relation to current MR-linac clinical workflows. We present a general dose accumulation framework for MRgART and discuss relevant quality assurance criteria. Finally, we highlight the clinical importance of dose accumulation in the ART era as well as the possible ways in which dose accumulation can transform clinical practice and improve our ability to deliver personalized RT.

Control of transit doses by Monte Carlo simulation inside an ionization casemate housing of a 60Co gamma irradiator

To increase the quality of irradiation at the Moroccan National Institute for Agronomic Research (NIAR) and meet customer needs, A calculation of the transit doses in the NIAR ionization casemate housing of a 60Co gamma irradiator was done in this work using Monte Carlo simulation with GATE/GEANT4 code. These additional doses called in this study transit doses come from the movement of the source cage carrying 8 60Co rods from their position in storage mode to their irradiation position then, from their irradiation position to their position in storage mode at the end of the irradiation process. In this work, the dosimetric mapping of transit doses and the isodose curves in the horizontal plane passing through the irradiation position of the source cage were established. In addition, a calculation of these doses for products placed on two turntables arranged around the NIAR gamma irradiator was also made in this work. The calculations are made taking into account three parameters, namely the number of irradiation fractions, the distance from the irradiator and the activity of the source. The results obtained showed that for a number of fractions of irradiations equal to 2, the transit doses vary between 0.22Gy and 5.68Gy for products placed on one of the two turntables at a distance varying between 47 cm and 147 cm from the gamma irradiator.