The accuracy of the system model in an iterative reconstruction algorithm greatly affects the quality of reconstructed
PET images. For efficient computation in reconstruction, the system model in PET can be factored into a product of
geometric projection matrix and detector blurring matrix, where the former is often computed based on analytical
calculation, and the latter is estimated using Monte Carlo simulations. In this work, we propose a method to estimate the
2D detector blurring matrix from experimental measurements. Point source data were acquired with high-count statistics
in the microPET II scanner using a computer-controlled 2-D motion stage. A monotonically convergent iterative
algorithm has been derived to estimate the detector blurring matrix from the point source measurements. The algorithm
takes advantage of the rotational symmetry of the PET scanner with the modeling of the detector block structure. Since
the resulting blurring matrix stems from actual measurements, it can take into account the physical effects in the photon
detection process that are difficult or impossible to model in a Monte Carlo simulation. Reconstructed images of a line
source phantom show improved resolution with the new detector blurring matrix compared to the original one from the
Monte Carlo simulation. This method can be applied to other small-animal and clinical scanners.
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