@misc{oai:repo.qst.go.jp:00068757,
 author = {Shidahara, Miho and Ikoma, Youko and Seki, Chie and Fujimura, Yota and Yoshida, Kinei and Ito, Hiroshi and Suhara, Tetsuya and Kanno, Iwao and 志田原 美保 and 生駒 洋子 and 関 千江 and 藤村 洋太 and 吉田 欣永 and 伊藤 浩 and 須原 哲也 and 菅野 巖},
 month = {Nov},
 note = {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 Conference},
 title = {Wavelet Denoising of Dynamic PET Data: Application to the Parametric Imaging of Peripheral Benzodiazepine Receptor},
 year = {2006}
}