Mar 16 – 17, 2021
Remote only
Europe/Paris timezone

Extended sources reconstructions by means of Coded mask aperture systems and Deep learning algorithm

Mar 17, 2021, 10:30 AM
15m
Remote only

Remote only

Speaker

Dr Geoffrey Daniel (CEA/DES/ISAS/DM2S/STMF/LGLS)

Description

The localization of radioactive sources provides mandatory information for the monitoring and the diagnostic of radiological scenes and it still constitutes a critical challenge. Gamma-ray imaging is performed through coded mask aperture imaging when the energy of the photons is sufficiently low to insure photoelectric interactions into the mask. Then, classically, a deconvolution algorithm is applied to reconstruct the position of the source. However, this deconvolution problem is non-injective and classical methods do not provide any relevant information when the source cannot be associated to a point, with respect to the angular resolution of the imaging system. In this presentation, we introduce a new method based on Deep Learning algorithms and Convolutional Neural Network. We evaluate its performances on extended sources with real measurements acquired with Caliste, a CdTe pixelated detector, and compare them to MLEM, a classical iterative algorithm.

Authors

Dr Geoffrey Daniel (CEA/DES/ISAS/DM2S/STMF/LGLS) Olivier Limousin (CEA SACLAY)

Presentation materials