Incorporation of wavelet-based denoising in iterative deconvolution for partial volume correction in whole-body PET imaging

Eur J Nucl Med Mol Imaging. 2009 Jul;36(7):1064-75. doi: 10.1007/s00259-009-1065-5. Epub 2009 Feb 18.


Purpose: 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.

Materials and 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.

Conclusion: The appropriate combination of deconvolution and wavelet-based denoising is an efficient method for reducing PVEs in emission tomography.

MeSH terms

  • Algorithms
  • Fluorodeoxyglucose F18
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Positron-Emission Tomography / methods*
  • Sensitivity and Specificity
  • Whole Body Imaging / methods*


  • Fluorodeoxyglucose F18