[A new Bayesian reconstruction algorithm for PET images based on correction of the detected sinogram data].
OBJECTIVE
To improve Bayesian reconstruction of positron-emission tomography (PET) images by devising a novel coupled feedback (CF) iterative model.


METHODS
The CF iterative algorithm was applied to update the noisy detected emission sinogram data using the latest reconstructed image in the iterative process of PET reconstruction. The relevant operations included linear filtering, wiener filtering, and projection of the reconstructed images. The sinogram data used in the study was obtained from simulated phantom data.


RESULTS
The experiments and corresponding visional and quantitative comparisons showed that the new method had better performance than the traditional Bayesian reconstruction of PET images for improvement of the PET images.


CONCLUSIONS
The proposed sinogram-correcting method allows improvement on the original measurement data, and is applicable for PET image reconstruction or other reconstruction tasks with high noise level.
Chen
Yang
Y
School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China. chenyang20021979@163.com
Wang
Qing-qi
QQ
Huang
Zhi-yong
ZY
Chen
Wu-fan
WF
D000465:Algorithms; D001499:Bayes Theorem; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008962:Models, Theoretical; D049268:Positron-Emission Tomography / Q000379:methods*