Dynamic PET Image reconstruction for parametric imaging using the HYPR kernel method

Benjamin Spencer, Jinyi Qi, Ramsey D. Badawi, Guobao Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


Dynamic PET image reconstruction is a challenging problem because of the ill-conditioned nature of PET and the lowcounting statistics resulted from short time-frames in dynamic imaging. The kernel method for image reconstruction has been developed to improve image reconstruction of low-count PET data by incorporating prior information derived from high-count composite data. In contrast to most of the existing regularization-based methods, the kernel method embeds image prior information in the forward projection model and does not require an explicit regularization term in the reconstruction formula. Inspired by the existing highly constrained back-projection (HYPR) algorithm for dynamic PET image denoising, we propose in this work a new type of kernel that is simpler to implement and further improves the kernel-based dynamic PET image reconstruction. Our evaluation study using a physical phantom scan with synthetic FDG tracer kinetics has demonstrated that the new HYPR kernel-based reconstruction can achieve a better region-of-interest (ROI) bias versus standard deviation trade-off for dynamic PET parametric imaging than the post-reconstruction HYPR denoising method and the previously used nonlocal-means kernel.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2017
Subtitle of host publicationPhysics of Medical Imaging
ISBN (Electronic)9781510607095
StatePublished - 2017
EventMedical Imaging 2017: Physics of Medical Imaging - Orlando, United States
Duration: Feb 13 2017Feb 16 2017


OtherMedical Imaging 2017: Physics of Medical Imaging
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Radiology Nuclear Medicine and imaging


Dive into the research topics of 'Dynamic PET Image reconstruction for parametric imaging using the HYPR kernel method'. Together they form a unique fingerprint.

Cite this