title: Applications of the line-of-response probability density function resolution model in PET list mode reconstruction.
abstract: Resolution degradation in PET image reconstruction can be caused by inaccurate modeling of the physical factors in the acquisition process. Resolution modeling (RM) is a common technique that takes into account the resolution degrading factors in the system matrix. Our previous work has introduced a probability density function (PDF) method of deriving the resolution kernels from Monte Carlo simulation and parameterizing the LORs to reduce the number of kernels needed for image reconstruction. In addition, LOR-PDF allows different PDFs to be applied to LORs from different crystal layer pairs of the HRRT. In this study, a thorough test was performed with this new model (LOR-PDF) applied to two PET scanners-the HRRT and Focus-220. A more uniform resolution distribution was observed in point source reconstructions by replacing the spatially-invariant kernels with the spatially-variant LOR-PDF. Specifically, from the center to the edge of radial field of view (FOV) of the HRRT, the measured in-plane FWHMs of point sources in a warm background varied slightly from 1.7 mm to 1.9 mm in LOR-PDF reconstructions. In Minihot and contrast phantom reconstructions, LOR-PDF resulted in up to 9% higher contrast at any given noise level than image-space resolution model. LOR-PDF also has the advantage in performing crystal-layer-dependent resolution modeling. The contrast improvement by using LOR-PDF was verified statistically by replicate reconstructions. In addition, [(11)C]AFM rats imaged on the HRRT and [(11)C]PHNO rats imaged on the Focus-220 were utilized to demonstrated the advantage of the new model. Higher contrast between high-uptake regions of only a few millimeter diameter and the background was observed in LOR-PDF reconstruction than in other methods.
authors.affiliation: Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
authors.forename: Y
authors.initials: Y
authors.lastname: Jian
authors.forename: R
authors.initials: R
authors.lastname: Yao
authors.forename: T
authors.initials: T
authors.lastname: Mulnix
authors.forename: X
authors.initials: X
authors.lastname: Jin
authors.forename: R E
authors.initials: RE
authors.lastname: Carson
mesh_terms: D000465:Algorithms; D000818:Animals; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016013:Likelihood Functions; D008962:Models, Theoretical; D009010:Monte Carlo Method; D016011:Normal Distribution; D019047:Phantoms, Imaging; D017785:Photons; D049268:Positron-Emission Tomography / Q000295:instrumentation / Q000379:methods*; D051381:Rats; D013116:Spinal Cord / Q000000981:diagnostic imaging*