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 | United States |
| City | San Diego, CA |
| Period | 10/29/06 → 11/4/06 |
Fingerprint
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Industrial and Manufacturing Engineering
Cite this
Spatially penalized methods for linear parametric imaging in dynamic PET. / Wang, Guobao; Qi, Jinyi.
IEEE Nuclear Science Symposium Conference Record. Vol. 3 2007. p. 1787-1791 4179354.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Spatially penalized methods for linear parametric imaging in dynamic PET
AU - Wang, Guobao
AU - Qi, Jinyi
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=38649087697&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=38649087697&partnerID=8YFLogxK
U2 - 10.1109/NSSMIC.2006.354241
DO - 10.1109/NSSMIC.2006.354241
M3 - Conference contribution
AN - SCOPUS:38649087697
SN - 1424405610
SN - 9781424405619
VL - 3
SP - 1787
EP - 1791
BT - IEEE Nuclear Science Symposium Conference Record
ER -