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

Abstract : 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.
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Submitted on : Friday, November 19, 2010 - 12:18:24 PM
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Nicolas Boussion, Catherine Cheze Le Rest, Mathieu Hatt, Dimitri Visvikis. Incorporation of wavelet-based denoising in iterative deconvolution for partial volume correction in whole-body PET imaging.. European Journal of Nuclear Medicine and Molecular Imaging, Springer Verlag (Germany), 2009, 36 (7), pp.1064-75. ⟨10.1007/s00259-009-1065-5⟩. ⟨inserm-00537782⟩

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