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 CEAList 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 illposed. 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 GaussMarkov field for the contrast distributions with a hidden PottsMarkov 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 »
 Authors:

 CEA, LIST, Laboratoire Images et Dynamique, 91191 GifsurYvette (France)
 Laboratoire des Signaux et Systemes, Unite mixte de recherche 8506 (CNRSSupelecUPS 11) Supelec, Plateau de Moulon, 3 rue JoliotCurie, 91191 GifsurYvette (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), 813 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 0094243X
 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 MohammadDjafari, Ali. Bayesian tomographic reconstruction of microsystems. United States: N. p., 2007.
Web. doi:10.1063/1.2821284.
Salem, Sofia Fekih, Vabre, Alexandre, & MohammadDjafari, Ali. Bayesian tomographic reconstruction of microsystems. United States. doi:10.1063/1.2821284.
Salem, Sofia Fekih, Vabre, Alexandre, and MohammadDjafari, 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 MohammadDjafari, 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 CEAList 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 illposed. 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 GaussMarkov field for the contrast distributions with a hidden PottsMarkov 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 = {0094243X},
number = 1,
volume = 954,
place = {United States},
year = {2007},
month = {11}
}