Regularization in tomographic reconstruction using thresholding estimators

Kalifa, Jerome; Laine, Andrew F.; Esser, Peter D.

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.


Also Published In

Wavelets--applications in signal and image processing IX : 30 July-1 August 2001, San Diego [Calif.], USA ; Proceedings of SPIE, vol. 4478

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Academic Units
Biomedical Engineering
Published Here
August 25, 2010