Title :
Regularization methods in iterative algorithms for variance reduction on compressed sinogram random coincidences
Author_Institution :
Mol. Imaging, Siemens Healthcare, Knoxville, TN, USA
fDate :
Oct. 24 2009-Nov. 1 2009
Abstract :
The Poisson model based image reconstruction algorithms in PET require the estimation of the mean value of random coincidences. The random data are often acquired with a delayed coincidence technique, and expected randoms are estimated through variance reduction (VR) of measured delayed coincidences. In the past we have developed iterative VR algorithms that estimate singles rates from random compressed sinograms. The final estimation of singles rates can be noisy in short scans. In the present work, we apply commonly used and specific regularization methods. A sequential coordinate ascent algorithm is used to maximize the penalized Poisson Likelihood objective function. Measured data from a Siemens TruePoint clinical scanner are used to validate the algorithm performance.
Keywords :
image reconstruction; iterative methods; medical image processing; positron emission tomography; random processes; PET; Poisson model; Siemens TruePoint clinical scanner; compressed sinogram random coincidences; delayed coincidences; image reconstruction; iterative algorithms; penalized Poisson likelihood objective function; regularization methods; variance reduction; Convergence; Data compression; Delay estimation; Equations; Frequency estimation; Image reconstruction; Iterative algorithms; Maximum likelihood estimation; Positron emission tomography; Virtual reality;
Conference_Titel :
Nuclear Science Symposium Conference Record (NSS/MIC), 2009 IEEE
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-4244-3961-4
Electronic_ISBN :
1095-7863
DOI :
10.1109/NSSMIC.2009.5401644