Penalized weighted least-squares image reconstruction for positron emission tomography
- Univ. of Michigan Medical Center, Ann Arbor, MI (United States)
This paper presents an image reconstruction method for positron-emission tomography (PET) based on a penalized, weighted least-squares (PWLS) objective. For PET measurements that are precorrected for accidental coincidences, the authors argue statistically that a least-squares objective function is as appropriate, if not more so, than the popular Poisson likelihood objective. The authors propose a simple data-based method for determining the weights that accounts for attenuation and detector efficiency. A non-negative successive over-relaxation (+SOR) algorithm converges rapidly to the global minimum of the PWLS objective. Quantitative simulation results demonstrate that the bias/variance trade-off of the PWLS + SOR method is comparable to the maximum-likelihood expectation-maximization (ML-EM) method (but with fewer iterations), and is improved relative to the conventional filtered backprojection (FBP) method. Qualitative results suggest that the streak artifacts common to the FBP method are nearly eliminated by the PWLS + SOR method, and indicate that the proposed method for weighting the measurements is a significant factor in the improvement over FBP.
- DOE Contract Number:
- FG02-87ER60561
- OSTI ID:
- 7082286
- Journal Information:
- IEEE Transactions on Medical Imaging (Institute of Electrical and Electronics Engineers); (United States), Vol. 13:2; ISSN 0278-0062
- Country of Publication:
- United States
- Language:
- English
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POSITRON COMPUTED TOMOGRAPHY
ACCURACY
ALGORITHMS
IMAGE PROCESSING
LEAST SQUARE FIT
COMPUTERIZED TOMOGRAPHY
DIAGNOSTIC TECHNIQUES
EMISSION COMPUTED TOMOGRAPHY
MATHEMATICAL LOGIC
MAXIMUM-LIKELIHOOD FIT
NUMERICAL SOLUTION
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