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Proceedings Paper

Regularization in tomographic reconstruction using thresholding estimators
Author(s): Jerome Kalifa; Andrew F. Laine; Peter D. Esser
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Paper Abstract

In tomographic medical devices such as SPECT or PET cameras, image reconstruction is an unstable inverse problem, due to the presence of additive noise. A new family of regularization methods for reconstruction, based on a thresholding procedure in wavelet and wavelet packet decompositions, is studied. This approach is based on the fact that the decompositions provide a near-diagonalization of the inverse Radon transform and of the prior information on medical images. An optimal wavelet packet decomposition is adaptively chosen for the specific image to be restored. Corresponding algorithms have been developed for both 2-D and full 3-D reconstruction. These procedures are fast, non-iterative, flexible, and their performance outperforms Filtered Back-Projection and iterative procedures such as OS-EM.

Paper Details

Date Published: 5 December 2001
PDF: 12 pages
Proc. SPIE 4478, Wavelets: Applications in Signal and Image Processing IX, (5 December 2001); doi: 10.1117/12.449736
Show Author Affiliations
Jerome Kalifa, Columbia Univ. (United States)
Andrew F. Laine, Columbia Univ. (United States)
Peter D. Esser, Columbia-Presbyterian Medical Ctr. (United States)

Published in SPIE Proceedings Vol. 4478:
Wavelets: Applications in Signal and Image Processing IX
Andrew F. Laine; Michael A. Unser; Akram Aldroubi, Editor(s)

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