OPTIMED: iterative optimization for large-scale inverse problems in medical imaging

被引：0

作者：

Chaari, Lotfi ^{[1
]}

Chouzenoux, Emilie

Pustelnik, Nelly ^{[3
]}

Chaux, Caroline ^{[2
]}

Moussaoui, Said ^{[4
]}

机构：

[1] INRIA Rhone Alpes, Projet MISTIS, Paris, France

[2] Univ Paris Est, IGM, UMR CNRS 8049, Lab Informat, Paris, France

[3] ENS Lyon, UMR CNRS 5672, Phys Lab, Lyon, France

[4] LUNAM Univ, Ecole Cent Nantes, IRCCyN, UMR CNRS 6597, Nantes, France

来源：

TRAITEMENT DU SIGNAL | 2011年 / 28卷 / 3-4期

关键词：

convex optimization; iterative algorithms; PET; MRI; 2D-NMR; CONJUGATE-GRADIENT METHODS; MAXIMUM-ENTROPY; THRESHOLDING ALGORITHM; INTEGRAL-EQUATIONS; GLOBAL CONVERGENCE; 1ST KIND; RECONSTRUCTION; REGULARIZATION; RECOVERY; MINIMIZATION;

D O I：

10.3166/TS.28.329-374

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper deals with image reconstruction using iterative algorithms. The framework under consideration involves large amount of data and/or large observation operator We proposed here algorithms for which we were able to state convergence proofs. The reliability and accuracy of the proposed approaches are demonstrated via three applications: the positron emission tomography (PET), the parallel magnetic resonance imaging (MRI) and two-dimensional nuclear magnetic resonance (2D NMR).

引用

页码：329 / 373

页数：45

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