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Title: Bayesian tomographic reconstruction of microsystems

Abstract

The microtomography by X ray transmission plays an increasingly dominating role in the study and the understanding of microsystems. Within this framework, an experimental setup of high resolution X ray microtomography was developed at CEA-List to quantify the physical parameters related to the fluids flow in microsystems. Several difficulties rise from the nature of experimental data collected on this setup: enhanced error measurements due to various physical phenomena occurring during the image formation (diffusion, beam hardening), and specificities of the setup (limited angle, partial view of the object, weak contrast).To reconstruct the object we must solve an inverse problem. This inverse problem is known to be ill-posed. It therefore needs to be regularized by introducing prior information. The main prior information we account for is that the object is composed of a finite known number of different materials distributed in compact regions. This a priori information is introduced via a Gauss-Markov field for the contrast distributions with a hidden Potts-Markov field for the class materials in the Bayesian estimation framework. The computations are done by using an appropriate Markov Chain Monte Carlo (MCMC) technique.In this paper, we present first the basic steps of the proposed algorithms. Then we focus onmore » one of the main steps in any iterative reconstruction method which is the computation of forward and adjoint operators (projection and backprojection). A fast implementation of these two operators is crucial for the real application of the method. We give some details on the fast computation of these steps and show some preliminary results of simulations.« less

Authors:
;  [1];  [2]
  1. CEA, LIST, Laboratoire Images et Dynamique, 91191 Gif-sur-Yvette (France)
  2. Laboratoire des Signaux et Systemes, Unite mixte de recherche 8506 (CNRS-Supelec-UPS 11) Supelec, Plateau de Moulon, 3 rue Joliot-Curie, 91191 Gif-sur-Yvette (France)
Publication Date:
OSTI Identifier:
21039276
Resource Type:
Journal Article
Journal Name:
AIP Conference Proceedings
Additional Journal Information:
Journal Volume: 954; Journal Issue: 1; Conference: 27. International workshop on Bayesian inference and maximum entropy methods in science and engineering, Saratoga Springs, NY (United States), 8-13 Jul 2007; Other Information: DOI: 10.1063/1.2821284; (c) 2007 American Institute of Physics; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0094-243X
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; 46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; ALGORITHMS; COMPUTERIZED SIMULATION; COMPUTERIZED TOMOGRAPHY; DIFFUSION; DISTRIBUTION; FLUID FLOW; IMAGE PROCESSING; ITERATIVE METHODS; MARKOV PROCESS; MONTE CARLO METHOD; RESOLUTION; X RADIATION

Citation Formats

Salem, Sofia Fekih, Vabre, Alexandre, and Mohammad-Djafari, Ali. Bayesian tomographic reconstruction of microsystems. United States: N. p., 2007. Web. doi:10.1063/1.2821284.
Salem, Sofia Fekih, Vabre, Alexandre, & Mohammad-Djafari, Ali. Bayesian tomographic reconstruction of microsystems. United States. doi:10.1063/1.2821284.
Salem, Sofia Fekih, Vabre, Alexandre, and Mohammad-Djafari, Ali. Tue . "Bayesian tomographic reconstruction of microsystems". United States. doi:10.1063/1.2821284.
@article{osti_21039276,
title = {Bayesian tomographic reconstruction of microsystems},
author = {Salem, Sofia Fekih and Vabre, Alexandre and Mohammad-Djafari, Ali},
abstractNote = {The microtomography by X ray transmission plays an increasingly dominating role in the study and the understanding of microsystems. Within this framework, an experimental setup of high resolution X ray microtomography was developed at CEA-List to quantify the physical parameters related to the fluids flow in microsystems. Several difficulties rise from the nature of experimental data collected on this setup: enhanced error measurements due to various physical phenomena occurring during the image formation (diffusion, beam hardening), and specificities of the setup (limited angle, partial view of the object, weak contrast).To reconstruct the object we must solve an inverse problem. This inverse problem is known to be ill-posed. It therefore needs to be regularized by introducing prior information. The main prior information we account for is that the object is composed of a finite known number of different materials distributed in compact regions. This a priori information is introduced via a Gauss-Markov field for the contrast distributions with a hidden Potts-Markov field for the class materials in the Bayesian estimation framework. The computations are done by using an appropriate Markov Chain Monte Carlo (MCMC) technique.In this paper, we present first the basic steps of the proposed algorithms. Then we focus on one of the main steps in any iterative reconstruction method which is the computation of forward and adjoint operators (projection and backprojection). A fast implementation of these two operators is crucial for the real application of the method. We give some details on the fast computation of these steps and show some preliminary results of simulations.},
doi = {10.1063/1.2821284},
journal = {AIP Conference Proceedings},
issn = {0094-243X},
number = 1,
volume = 954,
place = {United States},
year = {2007},
month = {11}
}