Abstract
Direct estimation of physiologically or biochemically important parameters from raw projection data is challenging in dynamic positron emission tomography (PET) due to the coupling between tomographic image reconstruction and nonlinear kinetic parameter estimation. Optimization transfer algorithms have been previously developed to solve the complex optimization problem. These algorithms, however, can suffer from slow convergence rate. This paper proposes an accelerated iterative algorithm for direct reconstruction of kinetic parameters through variable splitting under the framework of augmented Lagrangian optimization. Similar to the optimization transfer algorithms, the proposed algorithm splits each iteration of direct reconstruction into two separate steps: dynamic image reconstruction and pixel-wise nonlinear least squares kinetic fitting. The unique advantage of the new algorithm is its flexibility to employ any existing reconstruction algorithms in the reconstruction step, which can substantially accelerate the convergence speed. Computer simulations show that the proposed direct algorithm can be efficiently implemented and achieve much faster convergence speed than the optimization transfer algorithm.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - International Symposium on Biomedical Imaging |
| Publisher | IEEE Computer Society |
| Pages | 1200-1203 |
| Number of pages | 4 |
| Volume | 2015-July |
| ISBN (Print) | 9781479923748 |
| DOIs | |
| State | Published - Jul 21 2015 |
| Event | 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States Duration: Apr 16 2015 → Apr 19 2015 |
Other
| Other | 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 |
|---|---|
| Country | United States |
| City | Brooklyn |
| Period | 4/16/15 → 4/19/15 |
Fingerprint
Keywords
- Dynamic PET
- image reconstruction
- optimization algorithm
- parametric imaging
ASJC Scopus subject areas
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging
Cite this
Accelerated direct reconstruction of PET parametric images using augmented Lagrangian optimization. / Wang, Guobao; Qi, Jinyi.
Proceedings - International Symposium on Biomedical Imaging. Vol. 2015-July IEEE Computer Society, 2015. p. 1200-1203 7164088.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Accelerated direct reconstruction of PET parametric images using augmented Lagrangian optimization
AU - Wang, Guobao
AU - Qi, Jinyi
PY - 2015/7/21
Y1 - 2015/7/21
N2 - Direct estimation of physiologically or biochemically important parameters from raw projection data is challenging in dynamic positron emission tomography (PET) due to the coupling between tomographic image reconstruction and nonlinear kinetic parameter estimation. Optimization transfer algorithms have been previously developed to solve the complex optimization problem. These algorithms, however, can suffer from slow convergence rate. This paper proposes an accelerated iterative algorithm for direct reconstruction of kinetic parameters through variable splitting under the framework of augmented Lagrangian optimization. Similar to the optimization transfer algorithms, the proposed algorithm splits each iteration of direct reconstruction into two separate steps: dynamic image reconstruction and pixel-wise nonlinear least squares kinetic fitting. The unique advantage of the new algorithm is its flexibility to employ any existing reconstruction algorithms in the reconstruction step, which can substantially accelerate the convergence speed. Computer simulations show that the proposed direct algorithm can be efficiently implemented and achieve much faster convergence speed than the optimization transfer algorithm.
AB - Direct estimation of physiologically or biochemically important parameters from raw projection data is challenging in dynamic positron emission tomography (PET) due to the coupling between tomographic image reconstruction and nonlinear kinetic parameter estimation. Optimization transfer algorithms have been previously developed to solve the complex optimization problem. These algorithms, however, can suffer from slow convergence rate. This paper proposes an accelerated iterative algorithm for direct reconstruction of kinetic parameters through variable splitting under the framework of augmented Lagrangian optimization. Similar to the optimization transfer algorithms, the proposed algorithm splits each iteration of direct reconstruction into two separate steps: dynamic image reconstruction and pixel-wise nonlinear least squares kinetic fitting. The unique advantage of the new algorithm is its flexibility to employ any existing reconstruction algorithms in the reconstruction step, which can substantially accelerate the convergence speed. Computer simulations show that the proposed direct algorithm can be efficiently implemented and achieve much faster convergence speed than the optimization transfer algorithm.
KW - Dynamic PET
KW - image reconstruction
KW - optimization algorithm
KW - parametric imaging
UR - http://www.scopus.com/inward/record.url?scp=84944326916&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84944326916&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2015.7164088
DO - 10.1109/ISBI.2015.7164088
M3 - Conference contribution
AN - SCOPUS:84944326916
SN - 9781479923748
VL - 2015-July
SP - 1200
EP - 1203
BT - Proceedings - International Symposium on Biomedical Imaging
PB - IEEE Computer Society
ER -