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• W1601386084 abstract "A novel efficient hybrid maximum-likelihood/weighted least squares (ML/WLS) reconstruction algorithm for emission computed tomography is presented. The ML/WLS algorithm constructs optimized linear combinations of prior and current iterative image and projection estimates using a weighted least squares objective function. The optimized projections are backprojected with a maximum likelihood-expectation maximization (ML-EM) procedure. The ML/WLS method, including attenuation compensation, was used to reconstruct experimental single-photon emission computed tomography projection data obtained from a Hoffman brain phantom using the emission-transmission computed tomography system at the University of California, San Francisco. Results indicate that the ML/WLS procedure generates images qualitatively similar to those reconstructed with a conventional ML-EM algorithm, with significantly fewer iterations. >" @default.
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• W1601386084 date "2003-01-02" @default.
• W1601386084 modified "2023-09-23" @default.
• W1601386084 title "A hybrid maximum likelihood-least squares reconstruction algorithm for emission computed tomography" @default.
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• W1601386084 doi "https://doi.org/10.1109/nssmic.1992.301062" @default.
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