Abstract
A classic technique for reconstruction of Positive Emission Tomography (PET) images from measured projections is based on the maximum likelihood (ML) parameter estimation along with the Expectation Maximization (EM) algorithm. We incorporate the Wavelet transform (WT) into the ML framework, and obtain a new iterative algorithm that incorporates local and multiresolution properties of the WT within the structure of the EM. Using the WT allows one to embed regularization procedures (filtering) into the iterative process, by imposing a new set of parameters on a subset of wavelet coefficients with a desired resolution. Properties of the proposed algorithm are demonstrated on reconstructions of a synthetic brain phantom.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 90-93 |
| Number of pages | 4 |
| Journal | Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings |
| Volume | 1 |
| State | Published - 2000 |
| Externally published | Yes |
| Event | 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Chicago, IL, United States Duration: Jul 23 2000 → Jul 28 2000 |
Keywords
- Expectation maximization
- Medical imaging
- Positive emission tomography
- Wavelets
ASJC Scopus subject areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics