Convergence acceleration of the alternating minimization algorithm for high-speed gamma-ray tomography

• Autorzy: Maad R. , Johansen G. A.
• Języki publikacji: EN

Abstrakty

EN

A high-speed gamma-ray tomograph has been built to monitor dynamics processes. Unfortunately the use of a higher spatial and density resolution algorithm such the alternating minimization, takes a long time compared to the data acquisition time of the system, limiting thus the speed of the tomograph for real time imaging applications. This paper investigates the performance of different algorithms and combines fast image reconstruction algorithms with a higher spatial resolution algorithm to accelerate the convergence and keep a high spatial and density resolution of the system.

Słowa kluczowe

EN

gamma-ray tomography; error and image reconstruction; algorithm convergence and acceleration

• Wydawca: Wydawnictwo Politechniki Łódzkiej
• Czasopismo: Zeszyty Naukowe. Elektryka / Politechnika Łódzka
• Rocznik: 2009
• Tom: z. 114
• Strony: 173--176
• Opis fizyczny: Bibliogr. 9 poz.

Twórcy

autor

Maad R.

autor

Johansen G. A.

• University of Bergen, Department of Physics and Technology, Norway,

Bibliografia

• BHUSARAPU S., Al-DAHHAN M. H. AND DUDUKOVIC, (2005), Application of an Alternating Minimization Algorithm for image reconstruction in computer tomography, 4th World Congress on Industrial Process Tomography, Aizu, Japan, pp. 960-964.
• BYRNE C, (2005), Iterative Algorithms in Tomography URL: http://faculty.uml.edu/cbyrne/nlat.pdf.
• JOHANSEN G. A., FRØYSTEIN T., HJERTAKER B. T. AND OLSEN Ø. (1996), A dual sensor flow imaging tomographic system. (Invited) Measurement Science and Technology, Vol. 7, pp. 297-307.
• JOHANSEN G. A., FRØYSTEIN T., PEDERSEN H. and MCKIBBEN, (1997), Flexible Test Platform for Investigateing Gamma-ray Tomography Geometries and Applications Proc. Frontiers in Industrial Process Tomography II, Delft 8-12 April 1997, pp. 365-369.
• KAK A. AND SLANEY M., (2001), Principles of Computerized Tomographic Imaging, SIAM, Philadelphia, PA, pp. 275-296.
• MAAD R. and JOHANSEN G. A., (2003), Automatic weight matrix generation for gamma-ray tomography, 3rd World Congress on Industrial Process Tomography, Banff, Canada, pp. 86-89.
• MAAD R. and JOHANSEN G. A.,. (2006), Experimental noise analysis of reconstructed pixel values in high speed gamma-ray tomography, 4th International Symposium on Process Tomography, Poland, pp. 68-70.
• FESSLER J. A., (2006), Iterative Methods for image reconstruction, University of Michigan, EECS Department, URL:http://www.eecs.umich.edu/~fessler/papers/files/tal k/06/isbi,p 1 .note.pdf.
• VARMA R., BHUSARAPU S., O'SULLIVAN J. A. and Al-DAHHAN M. H., (2008), comparison of alternating minimization and expectation maximization algorithms for single source gamma ray tomography, Measurement Science and Technology , Vol. 19, 015506 (14p).