Abstract
Dynamic emission tomography can provide the estimation of physiological and biochemical parameters through the use of tracer kinetic modeling techniques. Compared to the conventional region-of-interest method for kinetic data analysis, parametric imaging is becoming preferable since it can provide the spatial distribution of physiologically important parameters. However, parametric images obtained by the conventional methods are usually noisy because the time activity curve of each pixel has high noise. Here we use Markov random field as an image prior to improve the quality of parametric images. Compared to the ridge regression method where spatial regularization was introduced through a mean image, Markov random field prior explicitly models the spatial correlation between neighboring pixels. We have derived monotonically convergent iterative algorithms for estimating parametric images. The method is evaluated using computer simulation and also applied to real dynamic PET data.
| Original language | English (US) |
|---|---|
| Title of host publication | IEEE Nuclear Science Symposium Conference Record |
| Pages | 1787-1791 |
| Number of pages | 5 |
| Volume | 3 |
| DOIs | |
| State | Published - 2007 |
| Event | 2006 IEEE Nuclear Science Symposium, Medical Imaging Conference and 15th International Workshop on Room-Temperature Semiconductor X- and Gamma-Ray Detectors, Special Focus Workshops, NSS/MIC/RTSD - San Diego, CA, United States Duration: Oct 29 2006 → Nov 4 2006 |
Other
| Other | 2006 IEEE Nuclear Science Symposium, Medical Imaging Conference and 15th International Workshop on Room-Temperature Semiconductor X- and Gamma-Ray Detectors, Special Focus Workshops, NSS/MIC/RTSD |
|---|---|
| Country/Territory | United States |
| City | San Diego, CA |
| Period | 10/29/06 → 11/4/06 |
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Industrial and Manufacturing Engineering