The computer aided collimation gamma camera (CACAO in French) is a gamma camera using a collimator with large holes, a supplementary linear scanning motion during the acquisition and a dedicated reconstruction program taking full account of the source depth. The CACAO system was introduced to improve both the sensitivity and the resolution in nuclear medicine. This thesis focuses on the design of a fast and robust reconstruction algorithm in the CACAO project. We start by an overview of tomographic imaging techniques in nuclear medicine. After modelling the physical CACAO system, we present the complete reconstruction program which involve three steps: 1) shift and sum 2) deconvolution and filtering 3) rotation and sum. The deconvolution is the critical step that decreases the signal to noise ratio of the reconstructed images. We propose a regularised multi-channel algorithm to solve the deconvolution problem. We also present a fast algorithm based on splines functions and preserving the high quality of the reconstructed images for the shift and the rotation steps. Comparisons of simulated reconstructed images in 2D and 3D for the conventional system (CPHC) and CACAO demonstrate the ability of CACAO system to increase the quality of the SPECT images. Finally, this study concludes with an experimental approach with a pixellated detector conceived for a 3D measurement of contaminated wounds. This experimentation proves the possible advantages of coupling the CACAO project with pixellated detectors. Moreover, a variety of applications could fully benefit from the CACAO system, such as low activity imaging, the use of high-energy gamma isotopes and the visualization of deep organs. Moreover the combination of the CACAO system with a pixels detector may open up further possibilities for the future of nuclear medicine.

Mots-clés: CACAO - tomographie d'émission monophotonique – collimation à trous larges - déconvolution - interpolation spline