Regularization in tomographic reconstruction using thresholding estimators

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

doi:10.1117/12.449736

5 December 2001 Regularization in tomographic reconstruction using thresholding estimators

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

Proceedings Volume 4478, Wavelets: Applications in Signal and Image Processing IX; (2001) https://doi.org/10.1117/12.449736
Event: International Symposium on Optical Science and Technology, 2001, San Diego, CA, United States

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.

Citation Download Citation

Jerome Kalifa, Andrew F. Laine, and Peter D. Esser "Regularization in tomographic reconstruction using thresholding estimators", Proc. SPIE 4478, Wavelets: Applications in Signal and Image Processing IX, (5 December 2001); https://doi.org/10.1117/12.449736

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