TY - GEN
T1 - Dynamic PET Image Reconstruction Using the Wavelet Kernel Method
AU - Ashouri, Zahra
AU - Hunter, Chad R.
AU - Spencer, Benjamin A.
AU - Wang, Guobao
AU - Dansereau, Richard M.
AU - Dekemp, Robert A.
PY - 2019/10
Y1 - 2019/10
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85083548721&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85083548721&partnerID=8YFLogxK
U2 - 10.1109/NSS/MIC42101.2019.9059890
DO - 10.1109/NSS/MIC42101.2019.9059890
M3 - Conference contribution
AN - SCOPUS:85083548721
T3 - 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019
BT - 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019
Y2 - 26 October 2019 through 2 November 2019
ER -