Regularization in tomographic reconstruction using thresholding estimators |
| |
Authors: | Kalifa Jérôme Laine Andrew Esser Peter D |
| |
Affiliation: | Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA. |
| |
Abstract: | In tomographic medical devices such as single photon emission computed tomography or positron emission tomography 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 (WP) decompositions, is studied. This approach is based on the fact that the decompositions provide a near-diagonalization of the inverse Radon transform and of prior information in medical images. A WP decomposition is adaptively chosen for the specific image to be restored. Corresponding algorithms have been developed for both two-dimensional and full three-dimensional reconstruction. These procedures are fast, noniterative, and flexible. Numerical results suggest that they outperform filtered back-projection and iterative procedures such as ordered-subset-expectation-maximization. |
| |
Keywords: | |
本文献已被 PubMed 等数据库收录! |
|