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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Politte, D.G. Snyder, D.L. |
| Copyright Year | 1991 |
| Description | Author affiliation: Washington Univ., Seattle, WA, USA (Politte, D.G.; Snyder, D.L.) |
| Abstract | Summary form only given, as follows. A method is proposed for improving estimates of radioactivity distributions obtained with PET (positron emission tomography) by utilizing anatomical information derived from MRI (magnetic resonance imaging) or X-ray CT (computered tomography). An algorithm is identified for computing regularized maximum-likelihood (ML) estimates of radioactivity distributions, subject to the constraint that a region of the image with known boundaries contains an unknown but constant intensity of radioactivity. Regularized ML estimates are computed using an expectation-maximization algorithm. A regularization method with spatially varying sieve and resolution kernels is used to prevent artifacts while preserving sharp boundaries where appropriate. The method is evaluated using a simulation and found to produce more accurate estimates than filtered backprojection or the usual ML algorithm. The variance of the constrained estimator is also studied. |
| File Size | 22976 |
| File Format | |
| ISBN | 0780305132 |
| DOI | 10.1109/NSSMIC.1991.259252 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1991-11-02 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image reconstruction Maximum likelihood estimation Positron emission tomography Magnetic resonance imaging Distributed computing X-ray imaging Computed tomography Expectation-maximization algorithms Spatial resolution Kernel |
| Content Type | Text |
| Resource Type | Article |
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