Artificial evolution for 3D PET reconstruction

Authors:
Franck P. Vidal;Delphine Lazaro-Ponthus;Samuel Legoupil;Jean Louchet;Évelyne Lutton;Jean-Marie Rocchisani
Affiliations:
INRIA Saclay, Île-de-France, APIS, Parc Orsay Université, Orsay Cedex, France and CEA, LIST, Saclay, Gif-sur-Yvette, France;CEA, LIST, Saclay, Gif-sur-Yvette, France;CEA, LIST, Saclay, Gif-sur-Yvette, France;INRIA Saclay, Île-de-France, APIS, Parc Orsay Université, Orsay Cedex, France and Artenia, Châtillon, France;INRIA Saclay, Île-de-France, APIS, Parc Orsay Université, Orsay Cedex, France;INRIA Saclay, Île-de-France, APIS, Parc Orsay Université, Orsay Cedex, France and Paris XIII University, UFR SMBH & Avicenne hospital, Bobigny, France
Venue:
EA'09 Proceedings of the 9th international conference on Artificial evolution
Year:
2009

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Abstract

This paper presents a method to take advantage of artificial evolution in positron emission tomography reconstruction. This imaging technique produces datasets that correspond to the concentration of positron emitters through the patient. Fully 3D tomographic reconstruction requires high computing power and leads to many challenges. Our aim is to reduce the computing cost and produce datasets while retaining the required quality. Our method is based on a coevolution strategy (also called Parisian evolution) named "fly algorithm". Each fly represents a point of the space and acts as a positron emitter. The final population of flies corresponds to the reconstructed data. Using "marginal evaluation", the fly's fitness is the positive or negative contribution of this fly to the performance of the population. This is also used to skip the relatively costly step of selection and simplify the evolutionary algorithm.