Title :
Wavelet domain Bayesian method for high noise level PET image reconstruction
Author :
Xie, L. ; Hu, Y. ; Luo, L. ; Shu, H.
Author_Institution :
Laboratory of Image Science and Technology Southeast University, Nanjing 210096, China
Abstract :
In this paper, a new maximum a posterior(MAP) method for PET image reconstruction defined in wavelet domain is proposed. Compared to the conventional MAP methods with Markov Random Field (MRF) prior models, the proposed method, named WD-MAP method, has better performance in characterize both local and global feature of reconstructed image due to the wavelet transform. Wavelet packet decomposition strategy is applied to further improve the reconstruction quality. The convergence speed of WD-MAP method is accelerated by adopting conjugate gradient(CG) technique. Simulated experiment suggests that the proposed method offers competitive performance in PET image reconstruction.
Keywords :
Acceleration; Bayesian methods; Convergence; Image reconstruction; Markov random fields; Noise level; Positron emission tomography; Wavelet domain; Wavelet packets; Wavelet transforms; MAP; Markov random field; PET; reconstruction; tomography; wavelet; wavelet packet; Algorithms; Bayes Theorem; Brain; Computer Simulation; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Image Processing, Computer-Assisted; Imaging, Three-Dimensional; Markov Chains; Models, Statistical; Phantoms, Imaging; Photons; Positron-Emission Tomography; Reproducibility of Results;
Conference_Titel :
Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE
Conference_Location :
Vancouver, BC
Print_ISBN :
978-1-4244-1814-5
Electronic_ISBN :
1557-170X
DOI :
10.1109/IEMBS.2008.4649836
