Several investigations have been carried out by researchers over past two decades to evaluate and perform the reading of gel dosimeters for the three-dimensional measurement of radiation fields. Imaging of the gels has been successfully accomplished with clinical MRI and via laser-based optical scanning using transmission of the light. We report here the methodology and results of a preliminary study carried out to evaluate the utility of a new and simplified approach to make 3D imaging of gel radiation dosimeters based on the scattering light analysis. For the purpose of this initial investigation, nMAG gel has been studied by our method. All pictures were evaluated through a region-of-interest (ROI) analysis to obtain the average change in image density in each sample as a function of the radiation dose. These measured ROI values were subjected to any fit and given a calibration dose and a spatial resolution. This way, we performed a 3D reconstruction of a dosimeter gel.

In radiation therapy, the trade-off between accuracy and speed is the key of the algorithms used in Treatment Planning Systems (TPS). For photon beams, commercial solutions generally relies on analytic algorithms, biased Monte Carlo, or heavily parallelized Monte Carlo on Graphics Processing Units (GPU). Alternatively, we propose an algorithm using Artificial Neural Network (ANN) to compute the dose distributions resulting from ionizing radiations inside a phantom [1] , [2] . We present an evolution of this platform taking into account modulated field sizes and shapes, and various orientations of the beam to the phantom. Firstly, tomodensitometry-based phantoms are created to validate the dose distribution computed for a square beam in heterogeneous areas (head and neck, lungs). Secondly, IMRT treatments are simulated in these phantoms. To validate our approach, we compare our results with the Analytical Anisotropic Algorithm (AAA) and Monte Carlo simulations. Cross-comparisons are performed for square beams and IMRT treatments. The dose distributions are evaluated using gamma indices and profile extractions. The dose distributions computed from IMRT treatments require less than two minutes using a standard Central Processing Unit (CPU). We aim at providing a fast and accurate solution for TPS quality control.

Gafchromic (TM) films have become popular due to their ease of use and their near water equivalence. This last property is crucial for stereotactic small beam dosimetry as demonstrated in recent papers. An accurate bi-dimensional dose measurement with Gafchromic (TM) films is very challenging mainly because of the non-uniformity response of flatbed scanners (used for films digitalization) and their own non uniformity. The first proposal of this work is to develop bi-dimensional protocol for small beams and evaluate the associated uncertainty. The second proposal is to validate this protocol for the bi-dimensional measurements of treatment plans performed with the CyberKnife (R) system.First, the uniformity of an Epson V700 flatbed scanner and a batch of EBT3 Gafchromic (TM) films has been investigated. A "four films" dosimeter was designed to reduce the errors (statistic and systematic) due to their non-uniformity. Then, the "four films" dosimeter protocol in both a homogeneous (RW3 material) and heterogeneous (RW3, lung-like and bone-like materials) phantoms has been used to measure the bi-dimensional dose distributions of three simple CyberKnife (R) treatment plans. Two tumor locations (middle of the lung and near lung/bone interface) were considered for the heterogeneous phantom. These plans were achieved with the 10 mm fixed collimator and modeled with the PENELOPE Monte Carlo code in order to calculate accurate dose distributions. Finally, the "four films" bi-dimensional dose distributions were compared to the PENELOPE Monte Carlo simulations.Regarding the uncertainty associated to the bi-dimensional dose measurement protocol, the relative standard deviation sigma(D) on the dose was 1.2% in the range from 0.5 to 4.0 Gy. Regarding the protocol validation on CyberKnife (R) treatment plans, a very good agreement was found with all measurement points passing the {3% - 3 mm} Gamma Index criteria. (C) 2016 Elsevier Ltd. All rights reserved.

Introduction In radiation therapy, one of the most time consuming step is due to the Treatment Planning System (TPS) and quality control algorithms for the dose computations. Using Artificial Neural Network (ANN), it is possible to compute the doses inside a phantom resulting from ionizing radiations [ 1 Mathieu R. Martin E. Makovicka L. Gschwind R. Contassot-Vivier S. Bahi J. Calculations of dose distributions using a neural network model. Phys Med Biol. 2005; 50: 1019-1028 Crossref PubMed Scopus (15) Google Scholar , 2 Vasseur A. Makovicka L. Martin E. Sauget M. Contassot-Vivier S. et al. Dose calculations using artificial neural networks: a feasibility study for photon beams. NIM. B. 2008; 266: 1085-1093 Crossref Scopus (17) Google Scholar ]. Material and methods More precisely, once the ANN is trained, it can compute a dose for every voxels of the phantom according to the beam characteristics. The main advantage is the computation time: the time-consuming step is the training of the ANN, that has to be performed only once and offline. The execution time, i.e. the dose associated to a voxel, is very low. We present an evolution of the platform presented in [ [3] Sauget M. Laurent R. Henriet J. Salomon M. Gschwind R. et al. Artificial neural networks–ICANN 2010. Springer, Berlin Heidelberg2010: 261-266 Google Scholar ], to take into account various field sizes and shapes, and also consider different orientation of the beam to the phantom. To validate the ANN computations, we compare them with Monte Carlo simulations. Homogeneous phantoms are used to validate interpolations on various densities and depth. Then, realistic phantoms are created to validate the dose distribution computed for a square beam in complex areas (head and neck, lungs, prostatic area). Finally, IMRT treatment simulations are validated. Results We compare the results obtained using our approach, the Eclipse TPS (AAA), and Monte Carlo simulations for a 10 cm x 10 cm photon beam on phantoms extracted from clinical data. The same procedure is applied to validate IMRT treatments. To compare the dose distributions, 3D gamma indices are computed, as well as profile extractions. For IMRT treatments, the dose distribution is computed in less than one minute on a traditional computer. Conclusions We have demonstrated the performance of our dose distribution engine for IMRT. Our goal is to provide a fast and accurate solution for TPS quality control. Therefore, we are currently working on the development of arctherapy treatment simulations.

A new formalism for small field dosimetry has been proposed (Alfonso et al., 2008) with the concept of an additional correction factor (k(Qclin,Qmsr)(fdin),f(msr)) which accounts for possible changes in detector response with field size. The aim of this work was to evaluate the response of eight commercially available detectors, then to provide a set of correction factors for a 1000 MU/min CyberKnife (R) equipped with fixed collimators and to compare them with those obtained for the 800 MU/min CyberKnife (R) version. Measurements were performed on a 1000 MU/min CyberKnife (R) with several active detectors designed for small field dosimetry (two chambers (PTW 31014 and 31018), three high resolution diodes (PTW 60016, 60017 and Sun Nuclear EDGE), a natural diamond (PTW 60003)) and two passive dosimeters (Harshaw TLD-700 (LiF)-Li-7:Mg,Ti thermoluminescent micro-cube and EBT3 radiochromic films). The CyberKnife (R) as well as the diode detectors, the PinPoint chamber, the diamond and the LiF micro-cubes were modeled with the PENELOPE Monte Carlo code in order to calculate the output factors in a point-like voxel of water (OFMC,w). A set of k(Qclin,Qmsr)(fclin,fmsr) correction factors for the active detectors investigated is provided for the 1000 MU/min CyberKnife (R) order to be used with the new formalism. A difference up to 2.4%, 2.0 and 1.7% in the correction factor obtained for the two different CyberKnife (R) models is found for the PTW 60003, the PTW 60016 and the PTW 60017 respectively. Although this difference is small, we recommend using specific k(Qclin,Qmsr)(fclin,fmsr) correction factors for the 1000 MU/min CyberKnife (R) when they are available. (C) 2014 Elsevier Ltd. All rights reserved.

Dental prostheses made of high density material contribute to modify dose distribution in head and neck cancer treatment. Our objective is to quantify dose perturbation due to high density inhomogeneity with experimental measurements and Monte Carlo simulations. Firstly, measurements were carried in a phantom representing a human jaw with thermoluminescent detectors (GR200A) and EBT2 Gafchromic films in the vicinity of three samples: a healthy tooth, a tooth with amalgam and a Ni-Cr crown, irradiated in clinical configuration. Secondly, Monte Carlo simulations (BEAMnrc code) were assessed in an identical configuration. Experimental measurements and simulation results confirm the two well-known phenomena: firstly the passage from a low density medium to a high density medium induces backscattered electrons causing a dose increase at the interface, and secondly, the passage from a high density medium to a low density medium creates a dose decrease near the interface. So, the results show a 1.4% and 23.8% backscatter dose rise and attenuation after sample of 26.7% and 10.9% respectively for tooth with amalgam and crown compared to the healthy tooth. Although a tooth with amalgam has a density of about 12-13, the changes generated are not significant. However, the results for crown (density of 8) are very significant and the discordance observed may be due to calculation point size difference 0.8 mm and 0.25 mm respectively for TLD and Monte Carlo. The use of Monte Carlo simulations and experimental measurements provides objective evidence to evaluate treatment planning system results with metal dental prostheses.

Purpose: Advanced stereotactic radiotherapy (SRT) treatments require accurate dose calculation for treatment planning especially for treatment sites involving heterogeneous patient anatomy. The purpose of this study was to evaluate the accuracy of dose calculation algorithms, Raytracing and Monte Carlo (MC), implemented in the MultiPlan treatment planning system (TPS) in presence of heterogeneities. Methods: First, the LINAC of a CyberKnife radiotherapy facility was modeled with the PENELOPE MC code. A protocol for the measurement of dose distributions with EBT3 films was established and validated thanks to comparison between experimental dose distributions and calculated dose distributions obtained with MultiPlan Raytracing and MC algorithms as well as with the PENELOPE MC model for treatments planned with the homogenous Easycube phantom. Finally, bones and lungs inserts were used to set up a heterogeneous Easycube phantom. Treatment plans with the 10, 7.5 or the 5 mm field sizes were generated in Multiplan TPS with different tumor localizations (in the lung and at the lung/bone/soft tissue interface). Experimental dose distributions were compared to the PENELOPE MC and Multiplan calculations using the gamma index method. Results: Regarding the experiment in the homogenous phantom, 100% of the points passed for the 3%/3mm tolerance criteria. These criteria include the global error of the method (CT-scan resolution, EBT3 dosimetry, LINAC positionning …), and were used afterwards to estimate the accuracy of the MultiPlan algorithms in heterogeneous media. Comparison of the dose distributions obtained in the heterogeneous phantom is in progress. Conclusion: This work has led to the development of numerical and experimental dosimetric tools for small beam dosimetry. Raytracing and MC algorithms implemented in MultiPlan TPS were evaluated in heterogeneous media.

PURPOSE In a previous work, output ratio (ORdet) measurements were performed for the 800 MU/min CyberKnife(®) at the Oscar Lambret Center (COL, France) using several commercially available detectors as well as using two passive dosimeters (EBT2 radiochromic film and micro-LiF TLD-700). The primary aim of the present work was to determine by Monte Carlo calculations the output factor in water (OFMC,w) and the [Formula: see text] correction factors. The secondary aim was to study the detector response in small beams using Monte Carlo simulation. METHODS The LINAC head of the CyberKnife(®) was modeled using the PENELOPE Monte Carlo code system. The primary electron beam was modeled using a monoenergetic source with a radial gaussian distribution. The model was adjusted by comparisons between calculated and measured lateral profiles and tissue-phantom ratios obtained with the largest field. In addition, the PTW 60016 and 60017 diodes, PTW 60003 diamond, and micro-LiF were modeled. Output ratios with modeled detectors (ORMC,det) and OFMC,w were calculated and compared to measurements, in order to validate the model for smallest fields and to calculate [Formula: see text] correction factors, respectively. For the study of the influence of detector characteristics on their response in small beams; first, the impact of the atomic composition and the mass density of silicon, LiF, and diamond materials were investigated; second, the material, the volume averaging, and the coating effects of detecting material on the detector responses were estimated. Finally, the influence of the size of silicon chip on diode response was investigated. RESULTS Looking at measurement ratios (uncorrected output factors) compared to the OFMC,w, the PTW 60016, 60017 and Sun Nuclear EDGE diodes systematically over-responded (about +6% for the 5 mm field), whereas the PTW 31014 Pinpoint chamber systematically under-responded (about -12% for the 5 mm field). ORdet measured with the SFD diode and PTW 60003 diamond detectors were in good agreement with OFMC,w except for the 5 mm field size (about -7.5% for the diamond and +3% for the SFD). A good agreement with OFMC,w was obtained with the EBT2 film and micro-LiF dosimeters (deviation less than 1.4% for all fields investigated). [Formula: see text] correction factors for several detectors used in this work have been calculated. The impact of atomic composition on the dosimetric response of detectors was found to be insignificant, unlike the mass density and size of the detecting material. CONCLUSIONS The results obtained with the passive dosimeters showed that they can be used for small beam OF measurements without correction factors. The study of detector response showed that ORdet is depending on the mass density, the volume averaging, and the coating effects of the detecting material. Each effect was quantified for the PTW 60016 and 60017 diodes, the micro-LiF, and the PTW 60003 diamond detectors. None of the active detectors used in this work can be recommended as a reference for small field dosimetry, but an improved diode detector with a smaller silicon chip coated with tissue-equivalent material is anticipated (by simulation) to be a reliable small field dosimetric detector in a nonequilibrium field.

To optimize the delivery in lung radiation therapy, a better understanding of the tumor motion is required, on one hand, to have a better tumor-targeting efficiency, and on the other hand to avoid as much as possible normal tissues. The four-dimensional computed tomography (4D-CT) allows to quantify tumor motion, but due to artifacts, it introduces biases and errors in tumor localization. Despite this disadvantage, we propose a method to simulate lung motion based on data provided by the 4D-CT for several patients. To reduce uncertainties introduced by the 4D-CT scan, we conveniently treated data using artificial neural networks. More precisely, our approach consists of a data augmentation technique. The data resulting from this processing step are then used to build a training set for another artificial neural network that learns the lung motion. To improve the learning accuracy, we have studied the number of phases required to precisely describe the displacement of each point. Thus, from 1118 points scattered across five patients and defined over 8 or 10 phases, we obtained 5800 points from 50 phases. After training, the network is used to compute the positions of 40 points from five other patients on 10 phases. These points allow to quantify the prediction performance. In comparison with the original data, the ones issued from our treatment process provide a significant increase of the prediction accuracy: an average improvement of 16% can be observed. The motion computed for several points by the neural network that has learnt the lung one exhibits an hysteresis near the one given by the 4D-CT, with an error smaller than 1mm in the cranio-caudal axis.

One of the possibilities to enhance the accuracy of lung radiotherapy is to improve the understanding of the individual lung motion of each patient. Indeed, using this knowledge, it becomes possible to follow the evolution of the clinical target volume defined by a set of points according to the lung breathing phase. This paper presents an innovative method to simulate the positions of points in a person’s lungs for each breathing phase. Our method, based on an artificial neural network (ANN), allowed us to learn the lung motion of five different patients and then to simulate it accurately for three other patients using only beginning and end points. The training set for our ANN consisted of more than 1,100 points spread over ten breathing phases from the five patients on a specific area of the lungs. The points were defined by a medical expert. The first results are very promising: we obtain an average accuracy of 1.5 mm while the spatial resolution is 1 × 1 × 2.5 mm3. The accuracy of the method will be improved even more with additional data and providing complete lung coverage.

To optimize the monitoring of female workers, numerical calibration of in vivo counting systems was carried out using a library of 24 female thoracic phantoms with various chest girths (85-120) and cup sizes (A-F) combined with Monte Carlo simulations. The morphology-induced variations of counting efficiency were thus quantified and put into equation. This paper focused on establishing a simple method to correct the efficiency calibration curve obtained with the Livermore male phantom, taking into account the breast size. As a result, lung efficiency corrections were tabulated for the 24 designed female torso models and the AREVA NC in vivo counting system. Using the developed morphological equation the corrections were also given for breast sizes not included in the library. In this work, alternative detector positioning was also investigated to further increase the counting efficiency that is significantly reduced by female breasts. It was found that positioning the detectors in the back of the subjects significantly improve detection limits in the case of large breasts and such positioning is worth being further investigated. Finally, a case-base reasoning platform was developed to go much further towards personalized numerical calibrations.

L'acquisition du mouvement est de plus en plus souvent effectuée pour améliorer la balistique des traitements en radiothérapie externe. Cependant, elle est source d'une exposition supplémentaire pour le patient. Le développement de la plate-forme de simulation numérique NEMOSIS (NEural NEtwork MOtion SImulation System) ouvre la voie à l'optimisation de la dose en imagerie. Elle permet de générer un mouvement pulmonaire localisé et personnalisé à partir du modèle 3D du patient. Pour 3 patients test, 5 à 6 points anatomiques ont été simulés puis comparés aux tracés du radiothérapeute. Dans le cas le plus défavorable, les résultats ont montré une précision moyennée sur l'ensemble des points d'un patient et sur toutes les phases d'environ 3 mm avec une incertitude élargie de tracé égale à 1,5 mm (intervalle de confiance de 95 %) et une incertitude maximale de phase atteignant 6,53 mm. Une autre étude comparant les GTV ( Gross Tumor Volume) d'un radiothérapeute et ceux calculés par NEMOSIS a été également menée. Un indice de Dice stipulant une correspondance minimale de 0,80 a été calculé entre les deux types de volumes. Ces résultats font de NEMOSIS un outil très prometteur en tant qu'alternative aux imageries irradiantes.

Perspective of the NEMOSIS platform in the context of dose reduction in imaging. Motion acquisition is frequently performed to improve the treatment ballistics of external radiation therapy. Nevertheless, this acquisition is an additional source of radiation exposure for the patient. The development of the numerical simulation platform NEMOSIS (NEural NEtwork MOtion SImulation System) offers new perspectives for the dose optimization in imaging. It allows one to generate a localized and customized lung motion using a 3D model of the patient. For 3 patients, 5 to 6 anatomic points were simulated and compared with the points plotted by the radiation therapist. In the worst case, the results show an average accuracy of 3 mm for all the points of a patient, at every phase, with an expanded uncertainty of plotting equal to 1.5 mm (confidence interval of 95%) and a maximum uncertainty of 6.53 mm over one phase. Another study comparing the GTV (Gross Tumor Volume) of a radiation therapist and that computed by NEMOSIS was also carried out. A Dice index stating a minimum correspondence of 0.80 was computed between these two types of volumes. These results make NEMOSIS a very promising tool as an alternative solution to irradiation imaging.

In the case of a radiological emergency situation, involving accidental human exposure, it is necessary to establish as soon as possible a dosimetry evaluation. In most cases, this evaluation is based on numerical representations and models of the victims. Unfortunately, personalised and realistic human representations are often unavailable for the exposed subjects. Hence, existing models like the 'Reference Man' representative of the average male individual are used. However, the accuracy of the treatment depends on the similarity of the phantom to the victim. The EquiVox platform (Research of Equivalent Voxel phantom) developed in this work uses the case-based reasoning principles to retrieve, from a set of existing phantoms, the most adapted one to represent the victim. This paper introduces the EquiVox platform and gives the example of in vivo lung monitoring optimisation to prove its efficiency in choosing the right model. It also presents the artificial neural network tools being developed to adapt the model to the victim.

Une des possibilités pour améliorer la balistique d’une radiothérapie pulmonaire consiste à mieux connaître le mouvement des poumons propre à chaque patient. En effet, grâce à cette connaissance, il devient possible de suivre l’évolution du tracé du volume cible anatomoclinique (clinical target volume) défini par un ensemble de points en fonction de la phase respiratoire. Cet article présente l’étude de faisabilité d’une méthode originale pour simuler les positions de points dans les poumons pour toute phase respiratoire. Cette approche, basée sur un réseau de neurones artificiels, a permis d’apprendre le mouvement pulmonaire sur plusieurs personnes pour ensuite le simuler pour de nouveaux patients dont seules les informations en début et en fin de respiration sont connues. Le réseau de neurones a été entraîné sur plus de 600 points tracés par un médecin, répartis sur trois patients et concentrés sur une zone précise du poumon. Les premiers résultats ont été très prometteurs car une précision moyenne de 1 mm pour une résolution spatiale de 1 × 1 × 2,5 mm3 a été obtenue. Nous avons montré qu’il était possible de simuler le mouvement pulmonaire avec précision à l’aide d’un réseau de neurones artificiels. À l’avenir, nous souhaitons améliorer la précision de notre méthode à l’aide de nouvelles données et d’une couverture totale des poumons par les points de l’ensemble d’apprentissage. A way to improve the accuracy of lung radiotherapy for a patient is to get a better understanding of its lung motion. Indeed, thanks to this knowledge it becomes possible to follow the displacements of the clinical target volume (CTV) induced by the lung breathing. This paper presents a feasibility study of an original method to simulate the positions of points in patient's lung at all breathing phases. This method, based on an artificial neural network, allowed learning the lung motion on real cases and then to simulate it for new patients for which only the beginning and the end breathing data are known. The neural network learning set is made up of more than 600 points. These points, shared out on three patients and gathered on a specific lung area, were plotted by a MD. The first results are promising: an average accuracy of 1 mm is obtained for a spatial resolution of 1 × 1 × 2.5 mm3. We have demonstrated that it is possible to simulate lung motion with accuracy using an artificial neural network. As future work we plan to improve the accuracy of our method with the addition of new patient data and a coverage of the whole lungs.