Temporal wavelet denoising of PET sinograms and images
The level of noise in PET dynamic studies makes it difficult to provide accurate and robust kinetic parameters from time activity curves, particularly at the voxel level. Several approaches have been followed to lower noise: denoising reconstructed images with spatial wavelets, adding a priori information during reconstruction about the signal without noise, including the temporal dimension during reconstruction. In this work, we propose to use a temporal wavelet denoising approach, based on the characteristics of PET time activity curves in sinograms (or reconstructed images). This approach has recently been proposed in image processing and relies on discriminating signal from noise by including relevant “a priori” information (on the statistical distribution of the wavelet coefficient for a whole sinogram or reconstructed image), as well as appropriate noise formation model in the time domain. This approach is tested in a 2D spatial + 1D time Monte Carlo simulation mimicking brain, and compared with a standard denoising approach : SUREShrink. Preliminary results indicate that better performances are obtained for sinogram denoising with the proposed approach compared with SUREShrink, and that the resulting sinograms can be reconstructed with a weighted least-squares (WLS) algorithm for all techniques. Denoising in the reconstructed images with this approach was also investigated.