Adapted Convex Optimization Algorithm for Wavelet-Based Dynamic PET Reconstruction

Abstract : This work deals with Dynamic Positron Emission Tomography (PET) data reconstruction, considering time as an additional variable (space+time). A convex optimization approach closely related to a Bayesian framework is adopted. The objective function to be minimized is expressed in the wavelet-frame domain and is non-necessarily differentiable in order to promote sparsity. We propose an adapted version of Forward-Backward- Douglas-Rachford (FBDR) algorithm to solve the resulting min- imization problem. The effectiveness of this approach is shown with simulated dynamic PET data. Comparative results are also provided.
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Nelly Pustelnik, Caroline Chaux, Jean-Christophe Pesquet, F. Sureau, E. Dush, et al.. Adapted Convex Optimization Algorithm for Wavelet-Based Dynamic PET Reconstruction. Fully3D, Sep 2009, Beijing, China. 10pp. ⟨hal-00621954⟩

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