Abstract
We propose a family of new algorithms that can be viewed as a generalization of the Algebraic Reconstruction Techniques (ART). These algorithms can be tailored for trade-offs between convergence speed and memory requirement. They also can be made to include Gaussian a priori image models. A key advantage is that they can handle arbitrary data acquisition scheme. Approximations are required for practical sized image reconstruction. We discuss several approximations and demonstrate numerical simulation examples for computed tomography (CT) reconstructions.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 19-25 |
| Number of pages | 7 |
| Journal | Proceedings of SPIE - The International Society for Optical Engineering |
| Volume | 4792 |
| DOIs | |
| State | Published - Dec 1 2002 |
| Externally published | Yes |
| Event | Image Reconstruction from Incomplete Data II - Seattle, WA, United States Duration: Jul 8 2002 → Jul 9 2002 |
Keywords
- Algebraic reconstruction techniques
- Recursive least squares
- Tomography
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering