Anatomically-aided PET reconstruction using the kernel method
Journal
Physics in Medicine and Biology
Journal Volume
61
Journal Issue
18
Pages
6668-6683
Date Issued
2016
Author(s)
Abstract
This paper extends the kernel method that was proposed previously for dynamic PET reconstruction, to incorporate anatomical side information into the PET reconstruction model. In contrast to existing methods that incorporate anatomical information using a penalized likelihood framework, the proposed method incorporates this information in the simpler maximum likelihood (ML) formulation and is amenable to ordered subsets. The new method also does not require any segmentation of the anatomical image to obtain edge information. We compare the kernel method with the Bowsher method for anatomically-aided PET image reconstruction through a simulated data set. Computer simulations demonstrate that the kernel method offers advantages over the Bowsher method in region of interest quantification. Additionally the kernel method is applied to a 3D patient data set. The kernel method results in reduced noise at a matched contrast level compared with the conventional ML expectation maximization algorithm. ? 2016 Institute of Physics and Engineering in Medicine.
Subjects
Hospital data processing
Image segmentation
Maximum likelihood
Maximum principle
Positron emission tomography
Anatomical images
Anatomical information
Anatomical priors
Expectation-maximization algorithms
Penalized likelihood
PET image reconstruction
PET reconstruction
Region of interest
Image reconstruction
algorithm
brain
brain mapping
computer simulation
diagnostic imaging
human
image processing
positron emission tomography
procedures
software
Algorithms
Brain
Brain Mapping
Computer Simulation
Humans
Image Processing, Computer-Assisted
Positron-Emission Tomography
Software
SDGs
Type
journal article