Abstract
Positron Emission Tomography (PET) is a functional imaging modality widely used in oncology, cardiology, and neurology. It is highly sensitive, but suffers from relatively poor spatial resolution, as compared with anatomical imaging modalities, such as magnetic resonance imaging (MRI). With the recent development of combined PET/MR systems, we can improve the PET image quality by incorporating MR information. Previously we have used kernel learning to embed MR information in static PET reconstruction and direct Patlak reconstruction. Here we extend this method to direct reconstruction of nonlinear parameters in a compartment model by using the alternating direction of multiplier method (ADMM) algorithm. Simulation studies show that the proposed method can produce superior parametric images compared with existing methods.
| Original language | English (US) |
|---|---|
| Title of host publication | Medical Imaging 2017 |
| Subtitle of host publication | Physics of Medical Imaging |
| Publisher | SPIE |
| Volume | 10132 |
| ISBN (Electronic) | 9781510607095 |
| DOIs | |
| State | Published - Jan 1 2017 |
| Event | Medical Imaging 2017: Physics of Medical Imaging - Orlando, United States Duration: Feb 13 2017 → Feb 16 2017 |
Other
| Other | Medical Imaging 2017: Physics of Medical Imaging |
|---|---|
| Country | United States |
| City | Orlando |
| Period | 2/13/17 → 2/16/17 |
Fingerprint
Keywords
- Direct parametric reconstruction
- Dynamic PET
- Kernel method
- MRI prior
ASJC Scopus subject areas
- Atomic and Molecular Physics, and Optics
- Electronic, Optical and Magnetic Materials
- Biomaterials
- Radiology Nuclear Medicine and imaging
Cite this
Nonlinear PET parametric image reconstruction with MRI information using kernel method. / Gong, Kuang; Wang, Guobao; Chen, Kevin T.; Catana, Ciprian; Qi, Jinyi.
Medical Imaging 2017: Physics of Medical Imaging. Vol. 10132 SPIE, 2017. 101321G.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Nonlinear PET parametric image reconstruction with MRI information using kernel method
AU - Gong, Kuang
AU - Wang, Guobao
AU - Chen, Kevin T.
AU - Catana, Ciprian
AU - Qi, Jinyi
PY - 2017/1/1
Y1 - 2017/1/1
N2 - Positron Emission Tomography (PET) is a functional imaging modality widely used in oncology, cardiology, and neurology. It is highly sensitive, but suffers from relatively poor spatial resolution, as compared with anatomical imaging modalities, such as magnetic resonance imaging (MRI). With the recent development of combined PET/MR systems, we can improve the PET image quality by incorporating MR information. Previously we have used kernel learning to embed MR information in static PET reconstruction and direct Patlak reconstruction. Here we extend this method to direct reconstruction of nonlinear parameters in a compartment model by using the alternating direction of multiplier method (ADMM) algorithm. Simulation studies show that the proposed method can produce superior parametric images compared with existing methods.
AB - Positron Emission Tomography (PET) is a functional imaging modality widely used in oncology, cardiology, and neurology. It is highly sensitive, but suffers from relatively poor spatial resolution, as compared with anatomical imaging modalities, such as magnetic resonance imaging (MRI). With the recent development of combined PET/MR systems, we can improve the PET image quality by incorporating MR information. Previously we have used kernel learning to embed MR information in static PET reconstruction and direct Patlak reconstruction. Here we extend this method to direct reconstruction of nonlinear parameters in a compartment model by using the alternating direction of multiplier method (ADMM) algorithm. Simulation studies show that the proposed method can produce superior parametric images compared with existing methods.
KW - Direct parametric reconstruction
KW - Dynamic PET
KW - Kernel method
KW - MRI prior
UR - http://www.scopus.com/inward/record.url?scp=85020429584&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85020429584&partnerID=8YFLogxK
U2 - 10.1117/12.2254273
DO - 10.1117/12.2254273
M3 - Conference contribution
VL - 10132
BT - Medical Imaging 2017
PB - SPIE
ER -