Improving signal-to-noise ratio in FDG parametric images by cluster analysis
| Autor: | Yasuyuki Kimura, N.M. Alpert, M. Senda |
|---|---|
| Rok vydání: | 2003 |
| Předmět: |
medicine.diagnostic_test
business.industry Estimation theory Noise (signal processing) Physics::Medical Physics Pattern recognition computer.software_genre Signal-to-noise ratio Positron emission tomography Voxel medicine Cluster (physics) Computer vision Artificial intelligence Cluster analysis business computer Parametric statistics Mathematics |
| Zdroj: | 1999 IEEE Nuclear Science Symposium. Conference Record. 1999 Nuclear Science Symposium and Medical Imaging Conference (Cat. No.99CH37019). |
| DOI: | 10.1109/nssmic.1999.842788 |
| Popis: | The problems associated with forming parametric images from a positron emission tomography (PET) kinetic study are large noise levels in the voxel-based tissue time-activity curve (tTAC), and huge calculation times. A clustering based kinetics method is proposed for addressing these problems. In this method, voxels with the same shape are gathered and averaged before parameter estimation. Compared with an ordinal regions of interest (ROI) based non-linear parameter estimation method, parametric images with almost the same statistical reliability are obtained. In addition, the calculation time is very short (two minutes). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|