Calibration of the high dose rate flash therapy beam monitor of the IRAMIS facility
Ultra-flash beams are pulsed beams of high-energy electrons (over a hundred MeV) with pulse durations in the femto-second range. The IRAMIS facility (CEA Saclay) uses laser acceleration to produce this type of beam, with a view to their application in radiotherapy. The LNHB is in charge of establishing dosimetric traceability for the IRAMIS facility, and to do this it has to calibrate the facility's monitor. Current radiotherapy facilities are based on medical linear accelerators operating at energies of up to 18 MeV in electron mode. LNHB has such equipment. It is used to establish national references in terms of absorbed dose to water, under the conditions of the IAEA protocol TRS 398.
Establishing dosimetric traceability involves choosing the measurement conditions, knowing the transfer dosimeter characteristics used and any corrections to be applied to the measurements taking into account the differences between the IRAMIS Facility and those of LNHB.
POST-DOC/CDD X-ray tomography reconstruction based on Deep-Learning methods
CEA-LIST is developing the CIVA software platform, a benchmark for the simulation of non-destructive testing processes. In particular, it offers tools for X-ray and tomographic inspection which, for a given inspection, can simulate all radiographies, taking into account various associated physical phenomena, as well as the corresponding tomographic reconstruction. CEA-LIST also has an experimental platform for robotized X-ray tomography inspection.
The proposed work is part of the laboratory's contribution to a bilateral French-German ANR project involving academic and industrial partners, focusing on the inspection of large-scale objects using the robotized platform. A sufficient number of X-rays must be taken in order to carry out a 3D reconstruction of the object. In many situations, some angles of view cannot be acquired due to the dimensions of the object and/or the motion limitations of the robots used, resulting in a loss of quality in the 3D reconstruction.
Expected contributions focus on the use of Deep-Learning methods, to complete missing projections on the one hand, and reduce reconstruction artifacts on the other. This work includes the CIVA-based steps of building a simulated database and evaluating the obtained results using POD (Probability Of Detection) measurements.
The candidate will have access to the facilities of the Paris Saclay research center and will be expected to promote his/her results in the form of scientific communications (international conferences, publications).
PhD in data processing or artificial intelligence.
Fluent English (oral presentations, scientific publications).
Previous knowledge of X-ray physics and tomographic reconstruction methods would be appreciated.
Optimization of a metrological approach to radionuclide identification based on spectral unmixing
The Laboratoire national Henri Becquerel (LNE-LNHB) at CEA/Saclay is the laboratory responsible for French references in the field of ionizing radiations. For several years now, it has been involved in the development of an automatic analysis tool for low-statistics gamma spectra, based on the spectral unmixing technique. This approach makes it possible to respond to metrological constraints such as robust decision-making and unbiased estimation of counts associated with identified radionuclides. To extend this technique to field measurements, and in particular to the deformation of spectra due to interactions in the environment of a radioactive source, a hybrid spectral unmixing model combining statistical and automatic learning methods is currently being developed. The aim of this mathematical solution is to implement a joint estimation of the spectra measured and the counts associated with the radionuclides identified. The next step will be to quantify the uncertainties of the quantities estimated from the hybrid model. The aim is also to investigate the technique of spectral unmixing in the case of neutron detection with a NaIL detector. The future candidate will contribute to these various studies in collaboration with the Laboratoire d'ingénierie logicielle pour les applications scientifiques (CEA/DRF).
Development of Algorithms for the Detection and Quantification of Biomarkers from Voltammograms
The objective of the post-doctoral research is to develop a high-performance algorithmic and software solution for the detection and quantification of biomarkers of interest from voltammograms. These voltammograms are one-dimensional signals obtained from innovative electrochemical sensors. The study will be carried out in close collaboration with another laboratory at CEA-LIST, the LIST/DIN/SIMRI/LCIM, which will provide dedicated and innovative electrochemical sensors, as well as with the start-up USENSE, which is developing a medical device for measuring multiple biomarkers in urine.
Detection of traces of narcotics in saliva by electrochemiluminescence on diamond electrodes
The consumption of narcotics is becoming a problem for road safety because 23% of road deaths in France occur in an accident involving at least one driver who tested positive. Thus, one objective of road safety in consultation with the concerned ministries (Ministry of Transport, Ministry of Interior, Ministry of Health and Ministry of Economy) is to improve the fight against road insecurity linked to narcotics consumption. In particular, this involves increasing and facilitating roadside checks using a portable device dedicated to controlling the use of narcotics on the roadside, similar to what is already done for breathalyzer tests. Such a device is not commercially available today. The main prerequisites of this device will be to provide reliable, immediate confirmation results with evidentiary value for the courts as well as a purchase cost compatible with large-scale deployment on French road networks. In this context, the subject of study proposed aims to study the possible detection of traces of narcotics in saliva using electroluminescence on a boron-doped diamond electrode. This method is considered promising for such an application because it potentially allows extremely low detection thresholds to be reached and, in accordance with legislative requirements, offers multiple possibilities aimed at achieving high selectivity towards chemical targets, with a high detection capacity. miniaturization of equipment and a relatively low cost of apparatus compared to analytical tools such as mass spectrometer, IMS, etc.
X-ray tomography reconstruction based on analytical methods and Deep-Learning
CEA-LIST develops the CIVA software platform, a reference for the simulation of non-destructive testing processes. In particular, it proposes tools for X-ray and tomographic inspection, which allow, for a given tomographic testing, to simulate all the radiographic projections (or sinogram) taking into account various associated physical phenomena, as well as the corresponding tomographic reconstruction.
The proposed work is part of the laboratory's contribution to a European project on tomographic testing of freight containers with inspection systems using high-energy sources. The spatial constraints of the projection acquisition stage (the trucks carrying the containers pass through an inspection gantry) imply an adaptation of the geometry of the source/detector system and consequently of the corresponding reconstruction algorithm. Moreover, the system can only generate a reduced number of projections, which makes the problem ill-posed in the context of inversion.
The expected contributions concern two distinct aspects of the reconstruction methodology from the acquired data. On the one hand, it is a question of adapting the analytical reconstruction methods to the specific acquisition geometry of this project, and on the other hand, to work on methods allowing to overcome the lack of information related to the limited number of radiographic projections. In this objective, supervised learning methods, more specifically by Deep-Learning, will be used both to complete the sinogram, and to reduce the reconstruction artifacts caused by the small number of projections available. A constraint of adequacy to the data and the acquisition system will also be introduced in order to generate physically coherent projections.