Abstract
The aim of this paper is to introduce methods for estimating mixture probabilities which specify time-activity curves (distributions of activity in time) in sequences of medical images. These parameters are of medical significance as they represent metabolic processes (functioning) occurring within each voxel within an imaged region within the body. Each distribution of activity by time is modelled as a mixture of specified (basis) components. The component distributions are those in a large (spectral) class of specified densities, for instance, exponential decay distributions with specified half-life. We describe the integration of maximum likelihood expectation maximization (ML-EM) reconstruction from indirect data with mixture modelling of the original time activity, and assess our ability to determine the distribution of this activity, varying in space and time.
| Original language | English |
|---|---|
| Pages (from-to) | 323-335 |
| Number of pages | 13 |
| Journal | Computational Statistics and Data Analysis |
| Volume | 32 |
| Issue number | 3-4 |
| Publication status | Published - 28 Jan 2000 |
Keywords
- Emission tomography
- EMPIRA
- ML-EM
- NNLS
- Spectral model