Development of a new CBR-based platform for human contamination emergency situations

• Published in: Radiation protection dosimetry Vol. 144; no. 1-4; pp. 564 - 570
• Main Authors: Farah, J., Henriet, J., Broggio, D., Laurent, R., Fontaine, E., Chebel-Morello, B., Sauget, M., Salomon, M., Makovicka, L., Franck, D.
• Format: Journal Article
• Language: English
• Published: England Oxford University Press (OUP) 01.03.2011
• Subjects: Algorithms; Artificial intelligence; Contamination; Disaster Planning; Dosimetry; Humans; Lung; Magnetic Resonance Imaging - methods; Male; Medical Physics; Models, Statistical; Models, Theoretical; neural networks; Neural Networks (Computer); Phantoms, Imaging; Physics; Radiation Monitoring - instrumentation; Radiation Monitoring - methods; Radiation Protection - instrumentation; Radiation Protection - methods; Radioactive Hazard Release; Radiometry - instrumentation; Radiometry - methods; Tomography, X-Ray Computed - methods
• ISSN: 0144-8420; 1742-3406; 1742-3406
• DOI: 10.1093/rpd/ncq440

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.