Title :
Total variation regulated EM algorithm [SPECT reconstruction]
Author :
Panin, V.Y. ; Zeng, G.L. ; Gullberg, G.T.
Author_Institution :
Dept. of Radiol., Utah Univ., Salt Lake City, UT, USA
Abstract :
An iterative Bayesian reconstruction algorithm based on the total variation (TV) norm constraint is proposed. The motivation for using TV regularization is that it is extremely effective for recovering edges of images. This paper extends the TV norm minimization constraint to the field of SPECT image reconstruction with a Poisson noise model. The regularization norm is included in the OSL-EM (one step late expectation maximization) algorithm. Unlike many other edge-preserving regularization techniques, the TV based method depends one parameter. Reconstructions of computer simulations and patient data show that the proposed algorithm has the capacity to smooth noise and maintain sharp edges without introducing over/under shoots and ripples around the edges.
Keywords :
Bayes methods; image reconstruction; inverse problems; iterative methods; medical image processing; modelling; single photon emission computed tomography; Poisson noise model; SPECT; edge-preserving regularization techniques; image edge recovery; iterative Bayesian reconstruction algorithm; medical diagnostic imaging; nuclear medicine; one step late expectation maximization algorithm; over/under shoots; ripples; total variation regulated EM algorithm; under shoots; Bayesian methods; Cities and towns; Image reconstruction; Iterative algorithms; Probability distribution; Radiology; Reconstruction algorithms; Senior members; Student members; TV;
Journal_Title :
Nuclear Science, IEEE Transactions on
