Abstract
Electrical Impedance Tomography is an emerging imaging modality based on reconstruction of the resistivity distribution of the interior of the body, using non-invasive surface electrical measurements. Image reconstruction is a nonlinear, ill-posed, inverse problem. A new approach to the reconstruction problem is proposed, using multi-layer feed forward neural networks. The technique was tested using numerical simulations and actual measurements from a physical model. When compared with iterative techniques, superior performance was observed vis-à-vis image quality, reconstruction time and robustness to noise. This method can be easily adapted for different forward models of resistivity distribution used in existing reconstruction algorithms.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 55-56 |
| Number of pages | 2 |
| Journal | Proceedings of the IEEE Annual Northeast Bioengineering Conference, NEBEC |
| Volume | 30 |
| State | Published - 2004 |
| Externally published | Yes |
| Event | Proceedings of the IEEE 30th Annual Northeast Bioengineering Conference - Springfield, MA, United States Duration: Apr 17 2004 → Apr 18 2004 |
ASJC Scopus subject areas
- Chemical Engineering(all)