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Neural networks in transmission and emission tomography

Grant number: 05/03414-4
Support type:Scholarships in Brazil - Master
Effective date (Start): May 01, 2006
Effective date (End): March 27, 2007
Field of knowledge:Physical Sciences and Mathematics - Mathematics - Applied Mathematics
Principal researcher:Alvaro Rodolfo de Pierro
Grantee:Juliana Verga
Home Institution: Instituto de Matemática, Estatística e Computação Científica (IMECC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil

Abstract

Emission Computed Tomography (ECT) aims at determining the emission densities of a metabolic compound tagged with a radioactive isotope from data obtained by detectors outside the body. Transmission tomography (CT) is the determination of the tissue atenuation inside the body. In the absence of errors both can be modelled by the Radon transform and the mathematical problem consists of computing its inverse. In practice many corrections are necessary to obtain reasonable images (high levels of attenuation in ECT, lack of data in CT, etc). These corrections avoid the necessity of dealing with a more realistic but nonlinear model. One alternative is to deal with the nonlinearities using artificial neural networks (ANN). We will study the development of appropriate ANNs for the case of estimating the attenuation from activity data only in ECT and for eliminating artifacts in the case of incomplete data in CT. (AU)

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