Dynamic PET Image Reconstruction Using the Wavelet Kernel Method
- Resource Type
- Conference
- Authors
- Ashouri, Zahra; Hunter, Chad R.; Spencer, Benjamin A.; Wang, Guobao; Dansereau, Richard M.; DeKemp, Robert. A.
- Source
- 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2019 IEEE. :1-3 Oct, 2019
- Subject
- Bioengineering
Components, Circuits, Devices and Systems
Computing and Processing
General Topics for Engineers
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Kernel
Image reconstruction
Positron emission tomography
Signal to noise ratio
Image quality
Standards
Heart
- Language
- ISSN
- 2577-0829
Dynamic PET imaging is used to monitor the spatio-temporal distribution of a tracer in a tissue region. Dynamic PET can suffer from high noise; to address this problem, the kernel method has been developed for efficient dynamic PET image reconstruction. Previous kernel approaches used a Gaussian kernel to exploit nonlocal spatial correlations from image priors. The Gaussian kernel, has an undesired effect of smoothing high frequencies. In this work, we propose using a wavelet kernel with good energy compaction to further enhance kernel-based dynamic PET image reconstruction. The oscillation in the wavelet kernel can result in better representation of details in the final reconstructed images. We evaluated the wavelet kernel approach using patient data acquired from dynamic C-11 hydroxyephedrine (HED) PET imaging. Reconstruction results demonstrate that this wavelet kernel approach achieves better image quality than standard reconstruction and the Gaussian kernel approaches.