Fast and accurate 3D X ray image reconstruction for Non Destructive Testing industrial applications

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Ajouté le: 5/02/2014
Directeur : MOHAMMAD-DJAFARI Ali - djafari@lss.supelec.fr
Titre : Fast and accurate 3D X ray image reconstruction for Non Destructive Testing industrial applications
Thèmes : Automatique, Signal, Télécoms, Systèmes embarqués
Laboratoires : L2S Laboratoire des Signaux et Systèmes UMR 8506
Description :

2D and 3D X ray Computed Tomography is very useful in medical imaging as well as in Non Destructive Testing (NDT) industrial applications. The main subjects of the research nowadays in medical imaging is the dose reduction while keeping the good quality of reconstructed images. In NDT, the main subject is NDT computational cost which needs to reduce the number of projections and develop fast methods for on line testing needs. The common point of both domains is limiting number of projections while proposing appropriate methods for fast and accurate reconstruction methods.

The Bayesian estimation approaches to the inverse problems of image reconstruction is appropriate for the above mentioned objective. This is due to the fact that this approach has the appropriate tools for combining a priori information as well as taking account for the measurement noise and modeling errors and giving an estimate and some measures of remaining uncertainties in the reconstruction results.

In L2S we have gained very high knowledge both in the main Bayesian approaches as well as in parallel implementation of the proposed algorithms on many core processors such as GPUs.

The main subject of this PhD proposal is, in a first step, to implement some of the main methods we have developed (Gauss-Markov prior modeling and MAP estimation) on GPU to evaluate the performances of these methods. Then, to adapt these methods to NDT applications, we have to use more appropriate priors such as Gauss-Markov-Potts or Infinite Gaussian Mixture (IGM) models. Then, the main difficulty becomes the computational costs. We need then to use Variational Bayesian Approximation (VBA) tools to be able to do fast computations. However, for real size 3D applications, we need to use muli-GPU and great amount of memory transfer optimization.

This PhD subject will be co-supervised by Ali Mohammad-Djafari who is a Research Director at CNRS and Nicolas Gac who is an Assistant Professor at the University of Paris Sud, Orsay. The main work will be done at Signal and system Laboratory (L2S) which is located at SUPELEC. The PhD is part of Doctoral program of “Université Paris Sud, Orsay”

Who can candidate :

A motivated Master level student with good backgrounds on Probability theory, Applied Mathematics, Signal and image processing, Pattern recognition and Bayesian inference methods and having skills in parallel computational algorithms and programming languages (C and Matlab).

Contacts :

Ali Mohammad-Djafari

djafari@lss.supelec.fr

0033 1 69851741

Nicolas Gac

Nicolas.Gac@lss.supelec.fr