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) |
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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 |
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Country/Territory | United States |
City | Orlando |
Period | 2/13/17 → 2/16/17 |
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