Image reconstruction for positron emission tomography (PET) can be challenging and the resulting image typically has high noise. The kernel-based reconstruction method [1], incorporates prior anatomic information in the reconstruction algorithm to reduce noise while preserving resolution. Prior information is incorporated in the reconstruction algorithm by means of spatial kernels originally used in machine learning. In this paper, the kernel-based method is used to reconstruct PET images of sympathetic innervation in the heart. The resulting images are compared with standard Ordered Subset Expectation Maximization (OSEM) reconstructed images qualitatively and quantitatively using data from 6 human subjects. The kernel-based method demonstrated superior SNR with preserved contrast and accuracy compared to OSEM.

Additional Metadata
Persistent URL dx.doi.org/10.1109/EMBC.2019.8856752
Conference 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Citation
Ashouri, Z. (Zahra), Hunter, C.R. (Chad R.), Spencer, B.A. (Benjamin A.), Wang, G. (Guobao), Dansereau, R, & Dekemp, R.A. (Robert A.). (2019). Kernel-Based Reconstruction of C-11-Hydroxyephedrine Cardiac PET Images of the Sympathetic Nervous System. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (pp. 832–835). doi:10.1109/EMBC.2019.8856752