Abstract：
:Regularization is desirable for image reconstruction in emission tomography. A powerful regularization method is the penalized-likelihood (PL) reconstruction algorithm (or equivalently, maximum a posteriori reconstruction), where the sum of the likelihood and a noise suppressing penalty term (or Bayesian prior) is optimized. Usually, this approach yields position-dependent resolution and bias. However, for some applications in emission tomography, a shift-invariant point spread function would be advantageous. Recently, a new method has been proposed, in which the penalty term is tuned in every pixel to impose a uniform local impulse response. In this paper, an alternative way to tune the penalty term is presented. We performed positron emission tomography and single photon emission computed tomography simulations to compare the performance of the new method to that of the postsmoothed maximum-likelihood (ML) approach, using the impulse response of the former method as the postsmoothing filter for the latter. For this experiment, the noise properties of the PL algorithm were not superior to those of postsmoothed ML reconstruction.
journal_name
IEEE Trans Med Imagingjournal_title
IEEE transactions on medical imagingauthors
Nuyts J,Fessler JAdoi
10.1109/TMI.2003.816960subject
Has Abstractpub_date
2003-09-01 00:00:00pages
1042-52issue
9eissn
0278-0062issn
1558-254Xjournal_volume
22pub_type
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journal_title：IEEE transactions on medical imaging
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