Incorporation of wavelet-based denoising in iterative deconvolution for partial volume correction in whole-body PET imaging.
OBJECTIVE
Partial volume effects (PVEs) are consequences of the limited resolution of emission tomography. The aim of the present study was to compare two new voxel-wise PVE correction algorithms based on deconvolution and wavelet-based denoising.


METHODS
Deconvolution was performed using the Lucy-Richardson and the Van-Cittert algorithms. Both of these methods were tested using simulated and real FDG PET images. Wavelet-based denoising was incorporated into the process in order to eliminate the noise observed in classical deconvolution methods.


RESULTS
Both deconvolution approaches led to significant intensity recovery, but the Van-Cittert algorithm provided images of inferior qualitative appearance. Furthermore, this method added massive levels of noise, even with the associated use of wavelet-denoising. On the other hand, the Lucy-Richardson algorithm combined with the same denoising process gave the best compromise between intensity recovery, noise attenuation and qualitative aspect of the images.


CONCLUSIONS
The appropriate combination of deconvolution and wavelet-based denoising is an efficient method for reducing PVEs in emission tomography.
Boussion
N
N
INSERM, U650, Laboratoire de Traitement de l'Information Médicale CHU MORVAN, Bat 2bis (I3S), 5 avenue Foch, Brest, 29609, France. nboussion@yahoo.fr
Cheze Le Rest
C
C
Hatt
M
M
Visvikis
D
D
D000465:Algorithms; D019788:Fluorodeoxyglucose F18; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D049268:Positron-Emission Tomography / Q000379:methods*; D012680:Sensitivity and Specificity; D051598:Whole Body Imaging / Q000379:methods*
D019788:Fluorodeoxyglucose F18