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Proceedings Paper

Inter-update Metz filtering as regularization for variable block-ART in PET reconstruction
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Paper Abstract

Positron Emission Tomography (PET) is a technology that uses short-lived radio nuclides altered by disease and precede changes that can be visualized by cross-sectional imaging. Over the last decade, this technique has become an important clinical tool for detection of tumors, follow-up treatment and drug research, providing an understanding of dynamic physiological processes. Since PET needs improved reconstruction algorithms to facilitate clinical diagnosis, we will investigate an improved iterative algorithm. Amongst current algorithms applied for PET reconstruction, ART was first proposed as a method of reconstruction from CT projections. With appropriate tuning, the convergence of these algorithms could be very fast indeed. However, the quality of reconstruction using these methods has not been thoroughly investigated. We study a variant of these algorithms. We present the state of the art, review well-known ART and investigate an optimum dynamically-changing block structure for the not yet fully explored variable-Block ART, which uses jointly the Inter-Update Metz filter for regularization and exploits the full symmetries in PET scanners. This reveals significant acceleration of initial convergence to an acceptable reconstruction of inconsistent cases. To assess the quality and analyze any discrepancy of the reconstructed images, two figures of merit (FOMs) are used to evaluate two 3D Data phantoms acquired on a GE-Advance scanner for high statistics.

Paper Details

Date Published: 11 March 2005
PDF: 10 pages
Proc. SPIE 5674, Computational Imaging III, (11 March 2005); doi: 10.1117/12.587631
Show Author Affiliations
Mustapha Sadki, Brunel Univ. (United Kingdom)
Maite Trujillo San-Martin, Brunel Univ. (United Kingdom)


Published in SPIE Proceedings Vol. 5674:
Computational Imaging III
Charles A. Bouman; Eric L. Miller, Editor(s)

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