Wavelet Denoising of Dynamic PET Data: Application to the Parametric Imaging of Peripheral Benzodiazepine Receptor

Introduction: Low signal-to-noise ratio (SNR) of time activity curve (TAC) at the voxel level causes severe bias and poor precision for estimated binding potential (BP) in peripheral benzodiazepine receptor (PBR) using Non-linear least square fitting. The purpose of this study is to evaluate noise reduction capability of wavelet denoising for estimated BP image. Methods: We applied spatial (3D) and temporal (frame) wavelet denoising to simulate data and clinical dynamic image of PBR using 18F-FEDAA1106. For denoising process, the way of thresholding wavelet coefficient (Daubecies 4) was soft-threshold for each subbands according to BayesShrink (Level4) for spatial and VISUShrink (Level3) for temporal. In simulation, we evaluated the effects of denoising in temporal dimension with irregular sampling interval. K values were mimicked human PBR study and 1000 of noisy TACs were generated (noise 1, 5, 10 and 15%). In clinical application, TAC and BP image with/without wavelet denoising for a subject were compared. Results: Wavelet denoising in temporal dimension improved overestimated BP value from 14.6% to 11.4% in case of noise level 10%. In clinical, wavelet denoising in spatial and temporal dimension can compensate the noise in TAC, however, difference between BP images of with/without wavelet denoising were not so apparent. Conclusion: Wavelet denoising improve the quality of dynamic images, however, further optimization will be required for improving SNR of BP images.IEEE 2006 Nuclear Science Symposium and Medical Imaging Conferenc