TY - GEN

T1 - Artificial Evolution Strategy for PET Reconstruction

AU - Vidal, F. P.

AU - Pavia, Y. L.

AU - Rocchisani, J.-M.

AU - Louchet, J.

AU - Lutton, É.

PY - 2013/5/1

Y1 - 2013/5/1

N2 - This paper shows new resutls of our artificial evolution algorithm for Positron Emission Tomography (PET) reconstruction. This imaging technique produces datasets corresponding to the concentration of positron emitters within the patient. Fully three-dimensional (3D) tomographic reconstruction requires high computing power and leads to many challenges. Our aim is to produce high quality datasets in a time that is clinically acceptable. Our method is based on a co-evolution strategy called the ``Fly algorithm''. Each fly represents a point in space and mimics a positron emitter. Each fly position is progressively optimised using evolutionary computing to closely match the data measured by the imaging system. The performance of each fly is assessed based on its positive or negative contribution to the performance of the whole population. The final population of flies approximates the radioactivity concentration. This approach has shown promising results on numerical phantom models. The size of objects and their relative concentrations can be calculated in two-dimensional (2D) space. In (3D), complex shapes can be reconstructed. In this paper, we demonstrate the ability of the algorithm to fidely reconstruct more anatomically realistic volumes.

AB - This paper shows new resutls of our artificial evolution algorithm for Positron Emission Tomography (PET) reconstruction. This imaging technique produces datasets corresponding to the concentration of positron emitters within the patient. Fully three-dimensional (3D) tomographic reconstruction requires high computing power and leads to many challenges. Our aim is to produce high quality datasets in a time that is clinically acceptable. Our method is based on a co-evolution strategy called the ``Fly algorithm''. Each fly represents a point in space and mimics a positron emitter. Each fly position is progressively optimised using evolutionary computing to closely match the data measured by the imaging system. The performance of each fly is assessed based on its positive or negative contribution to the performance of the whole population. The final population of flies approximates the radioactivity concentration. This approach has shown promising results on numerical phantom models. The size of objects and their relative concentrations can be calculated in two-dimensional (2D) space. In (3D), complex shapes can be reconstructed. In this paper, we demonstrate the ability of the algorithm to fidely reconstruct more anatomically realistic volumes.

KW - Evolutionary computation, inverse problems, adaptive algorithm, Nuclear medicine, Positron emission tomography, Reconstruction algorithms

M3 - Conference contribution

SN - 978-3-642-14155-3

T3 - Lecture Notes in Computer Science

SP - 37

EP - 48

BT - International Conference on Medical Imaging Using Bio-Inspired and Soft Computing (MIBISOC2013)

PB - Springer

CY - Brussels, Belgium

T2 - International Conference on Medical Imaging Using Bio-Inspired and Soft Computing

Y2 - 15 May 2013 through 17 May 2013

ER -