Modeling and incorporation of system response functions in 3-D whole body PET

Appropriate application of spatially variant system models can correct for degraded resolution response and mispositioning errors. This paper explores the detector blurring component of the system model for a whole body positron emission tomography (PET) system and extends this factor into a more ge...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2006-07, Vol.25 (7), p.828-837
Main Authors: Alessio, A.M., Kinahan, P.E., Lewellen, T.K.
Format: Article
Language:English
Subjects:
Algorithms
Computer Simulation
Degradation
Detector response
Detectors
Error correction
Fourier rebinning (FORE)
fully three-dimensional (3-D) positron emission tomography (PET)
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image reconstruction
Imaging, Three-Dimensional - methods
Information Storage and Retrieval - methods
Models, Biological
Neoplasms
Numerical Analysis, Computer-Assisted
Phantoms, Imaging
Positron emission tomography
Positron-Emission Tomography - instrumentation
Positron-Emission Tomography - methods
Reproducibility of Results
Scattering
Sensitivity and Specificity
Size measurement
Spatial resolution
Studies
system model
system response
Whole Body Imaging - methods
Whole-body PET
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Summary:Appropriate application of spatially variant system models can correct for degraded resolution response and mispositioning errors. This paper explores the detector blurring component of the system model for a whole body positron emission tomography (PET) system and extends this factor into a more general system response function to account for other system effects including the influence of Fourier rebinning (FORE). We model the system response function as a three-dimensional (3-D) function that blurs in the radial and axial dimension and is spatially variant in radial location. This function is derived from Monte Carlo simulations and incorporates inter-crystal scatter, crystal penetration, and the blurring due to the FORE algorithm. The improved system model is applied in a modified ordered subsets expectation maximization (OSEM) algorithm to reconstruct images from rebinned, fully 3-D PET data. The proposed method effectively removes the spatial variance in the resolution response, as shown in simulations of point sources. Furthermore, simulation and measured studies show the proposed method improves quantitative accuracy with a reduction in tumor bias compared to conventional OSEM on the order of 10%-30% depending on tumor size and smoothing parameter
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2006.873222