Respiratory movement information is useful for radiation therapy, and is generally obtained using 4D scanners (4DCT). In the interest of patient safety, reducing the use of 4DCT could be a significant step in reducing radiation exposure, the effects of which are not well documented. The authors propose a customized 4D numerical phantom representing the organ contours. Firstly, breathing movement can be simulated and customized according to the patient's anthroporadiametric data. Using learning sets constituted by 4D scanners, artificial neural networks can be trained to interpolate the lung contours corresponding to an unknown patient, and then to simulate its respiration. Lung movement during the breathing cycle is modeled by predicting the lung contours at any respiratory phases. The interpolation is validated comparing the obtained lung contours with 4DCT via Dice coefficient. Secondly, a preliminary study of cardiac and œsophageal motion is also presented to demonstrate the flexibility of this approach. The application may simulate the position and volume of the lungs, the œsophagus and the heart at every phase of the respiratory cycle with a good accuracy: the validation of the lung modeling gives a Dice index greater than 0.93 with 4DCT over a breath cycle.

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 (Sun Nuclear EDGE, IBA SFD, PTW 60016, PTW 60017, PTW 31014 and PTW 60003) designed for small beams as well as using two passive dosimeters (EBT2 radiochromic film and Harshaw micro-LiF TLD-700). The primary aim of this work was to determine by Monte Carlo calculations the output factor in water (OFMC,w) and to identify the detectors best suited to the measurements of output factors (OF) in small beams. The secondary aim was to calculate the correction factors; and also, to determine the detector response in small beams using Monte Carlo simulation.

BACKGROUND:Intensity Modulated Arc Therapy (IMAT) can be planned and delivered via several techniques. Advanced Radiotherapy (ARTORL) is a prospective study that aims to evaluate the treatment costs and clinical aspects of implementing these IMAT techniques for head and neck cancers. In this context, we evaluated the potential dosimetric gain of Helical Tomotherapy (TomoTherapy, Accuray, HT) versus VMAT (Rapid'Arc(®), Varian Medical System, RA) for oropharyngeal cancer (OC).MATERIAL AND METHODS:Thirty patients were selected from our database in whom bilateral neck irradiation and treatment to the primary were indicated. Each patient was planned twice using both HT and RA planning systems using a simultaneous integrated boost approach. For the planning target volumes (PTV) and organs at risk, ICRU 83 reporting guidelines were followed. RA and HT plans were compared using paired Student's t-test.RESULTS:RA and HT produced plans with a good coverage of PTVs and acceptable sparing of OARs. Although some dosimetric differences were statistically significant, they remained small. However, the near maximal dose to the PRV of spinal cord and brain stem was lower with HT. Regarding normal tissue, HT increased the volume irradiated at doses between 4 and 20 Gy compared to RA.CONCLUSION:In OC, HT and RA showed similar dosimetric results. They represent the maximum gains obtained with photon beams. The medicoeconomic evaluation of our study is ongoing and may reveal differences between these techniques in terms of MU number, fraction time, and clinical evaluation.

A dosimetric comparison was made of Helical Tomotherapy (HT) and Rapid'Arc(®) (RA) in 115 patients with head and neck carcinoma included in a prospective and multicentric study. HT and RA provided highly conformal plans that easily complied with dose volume constraints for organs at risk. HT reduced high doses to the planning target volumes (PTVs) compared to RA and provided a more homogeneous dose distribution but with an increased Non Tumoral Integral Dose (NTID) than RA. However, the clinical consequences of these dosimetric advantages and disadvantages need further investigation.

In case of a radiological emergency situation involving accidental human exposure, a dosimetry evaluation must be established as soon as possible. In most cases, this evaluation is based on numerical representations and models of victims. Unfortunately, personalized and realistic human representations are often unavailable for the exposed subjects. However, accuracy of treatment depends on the similarity of the phantom to the victim. The EquiVox platform (Research of Equivalent Voxel phantom) developed in this study uses case-based reasoning (CBR) principles to retrieve and adapt, from among a set of existing phantoms, the one to represent the victim. This paper introduces the EquiVox platform and the artificial neural network (ANN) developed to interpolate the victim's 3D lung contours. The results obtained for the choice and construction of the contours are presented and discussed.

In case of a radiological emergency situation involving accidental human exposure, a dosimetry evaluation must be established as soon as possible. In most cases, this evaluation is based on numerical representations and models of subjects. Unfortunately, personalised and realistic human representations are often unavailable for the exposed subjects. However, accuracy of treatment depends on the similarity of the phantom to the subject. The EquiVox platform (Research of Equivalent Voxel phantom) developed in this study uses Case-Based Reasoning principles to retrieve and adapt, from among a set of existing phantoms, the one to represent the subject. This paper introduces the EquiVox platform and Artificial Neural Networks developed to interpolate the subject’s 3D lung contours. The results obtained for the choice and construction of the contours are presented and discussed.

Actualy, about 40 % of the annual mean dose perceived by inhabitant (french statistics) is of medical origin by use of ionising radiations in diagnostic and therapeutic purposes (more than 50 % of cancers treated by rays). This increasing contribution year by year because of the reproduction and the rehearsal of exams can be better administered by the improvement of the purchase techniques notably by the association of physical means and of computing sciences. In this optics, we developed tools stemming from artificial intelligence in the service of the medical radiation physics and in the radiation protection.

Nous presentons la plate-forme de formation a distance CEFOR@D, dont le but est repondre aux besoins en formation dans les domaines de la radioprotection et de la radiophysique medicale.

The purpose of this work is to further study the relevance of accelerating the Monte Carlo calculations for the gamma rays external radiotherapy through feed-forward neural networks. We have previously presented a parallel incremental algorithm that builds neural networks of reduced size, while providing high quality approximations of the dose deposit. Our parallel algorithm consists in a regular decomposition of the initial learning dataset (also called learning domain) in as much subsets as available processors. However, the initial learning set presents heterogeneous signal complexities and consequently, the learning times of regular subsets are very different. This paper presents an efficient learning domain decomposition which balances the signal complexities across the processors. As will be shown, the resulting irregular decomposition allows for important gains in learning time of the global network.

In the domain of radiation protection it is not always possible to perform an additional examination such as scanners or Magnetic Resonance Imaging (MRI) for a patient who has been accidentally radiated. However a medical diagnostic must be made as soon as possible to calculate the dosimetric balance. Currently this incidental calculation is based on the available voxelised phantoms which are 3D numerical reconstructions of the human body with the internal organs. The Case-Based Reasoning (CBR) is seen on the one hand like a problem solving method and on the other hand like a technology for the conception of intelligent systems. The ReEPh project (Research of Equivalent Phantom) strikes a new path in the field of problem solving methods in the radiation protection and uses the approach of the CBR to retrieve the set of the phantoms the most adapted to the irradiated victim. For this first version of ReEPh, the retrieval phase uses a Knn Algorithm (K nearest neighbours). We propose a measure of similarity and a confidence index to take into account the uncertainty implied by the possible missing characteristics of the victim. We have developed a graphic interface to view the cases retrieval, and to visually illustrate the combination of measures of similarity and confidence index.

This document gathers the slides of the available presentations given during this conference. Sixty six presentations out of 68 are assembled in the document. The conference is organized in 11 sessions dealing with: 1 - radioprotection regulations and standards; 2 - radioprotection of patients; 3 - radiation effects on man and ecosystems; 4 - advances in dosimetry and metrology; occupational radioprotection (2 sessions); 5 - radioprotection of populations and ecosystems (2 sessions); 6 - radioprotection in incident, accident and post-accident situations; 7 - radioprotection and society

Direct dose calculations are a crucial requirement for Treatment Planning Systems. Some methods, such as Monte Carlo, explicitly model particle transport, others depend upon tabulated data or analytic formulae. However, their computation time is too lengthy for clinical use, or accuracy is insufficient, especially for recent techniques such as Intensity-Modulated Radiotherapy. Based on artificial neural networks (ANNs), a new solution is proposed and this work extends the properties of such an algorithm and is called NeuRad.Prior to any calculations, a first phase known as the learning process is necessary. Monte Carlo dose distributions in homogeneous media are used, and the ANN is then acquired. According to the training base, it can be used as a dose engine for either heterogeneous media or for an unknown material. In this report, two networks were created in order to compute dose distribution within a homogeneous phantom made of an unknown material and within an inhomogeneous phantom made of water and TA6V4 (titanium alloy corresponding to hip prosthesis).All NeuRad results were compared to Monte Carlo distributions. The latter required about 7 h on a dedicated cluster (10 nodes). NeuRad learning requires between 8 and 18 It (depending upon the size of the training base) on a single low-end computer. However, the results of dose computation with the ANN are available in less than 2 s, again using a low-end computer, for a 150 x 1 x 150 voxels phantom. In the case of homogeneous medium, the mean deviation in the high dose region was less than 1.7%. With a TA6V4 hip prosthesis bathed in water, the mean deviation in the high dose region was less than 4.1%.Further improvements in NeuRad will have to include full 3D calculations, inhomogeneity management and input definitions. (c) 2008 Elsevier B.V. All rights reserved.

Because of the ageing of the population, an increasing number of patients with hip prostheses are undergoing pelvic irradiation. Treatment planning systems (TPS) currently available are not always able to accurately predict the dose distribution around such implants. In fact, only Monte Carlo simulation has the ability to precisely calculate the impact of a hip prosthesis during radiotherapeutic treatment. Monte Carlo phantoms were developed to evaluate the dose perturbations during pelvic irradiation. A first model, constructed with the DOSXYZnrc usercode, was elaborated to determine the dose increase at the tissue–metal interface as well as the impact of the material coating the prosthesis. Next, CT-based phantoms were prepared, using the usercode CTCreate, to estimate the influence of the geometry and the composition of such implants on the beam attenuation. Thanks to a program that we developed, the study was carried out with CT-based phantoms containing a hip prosthesis without metal artefacts. Therefore, anthropomorphic phantoms allowed better definition of both patient anatomy and the hip prosthesis in order to better reproduce the clinical conditions of pelvic irradiation. The Monte Carlo results revealed the impact of certain coatings such as PMMA on dose enhancement at the tissue–metal interface. Monte Carlo calculations in CT-based phantoms highlighted the marked influence of the implant’s composition, its geometry as well as its position within the beam on dose distribution.

An efficient and accurate algorithm for radiation dose evaluation is presented in this paper. Such computations are useful in the radiotherapic treatment planning of tumors. The originality of our approach is to use a neural network which has been trained with several homogeneous environments to deduce the doses in any kind of environment (possibly heterogeneous). Our algorithm is compared in several representative contexts to a reference simulation code in the domain.