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Communication Dans Un Congrès Année : 2019

Dual approach for TV-regularized Maximum Likelihood Expectation Maximization in tomography with Poisson data

Résumé

Tomography in nuclear medicine requires resolution of a linear inverse problem. As the radioactive decay follows a Poisson law, the projections of gamma emission from inside the body are also Poisson distributed. In this talk we will introduce a dual algorithm for total variation (TV) Poisson denoising. When combined with MLEM (maximum likelihood expectation maximization) iterations, a fast and convergent algorithm for the estimation of the TV maximum-a-posteriori solution is obtained.
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Dates et versions

hal-02359496 , version 1 (12-11-2019)

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  • HAL Id : hal-02359496 , version 1

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Voichita Maxim, Elie Bretin. Dual approach for TV-regularized Maximum Likelihood Expectation Maximization in tomography with Poisson data. ICIAM, Jul 2019, Valencia, Spain. ⟨hal-02359496⟩
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