High-resolution image reconstruction for PET using estimated detector response functions

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.