  Positron Emission Tomography (PET) scanners are usually designed with the
goal to obtain the best compromise between sensitivity, resolution,
field-of-view size, and cost. Therefore, it is difficult to improve the
resolution of a PET scanner with hardware modifications, without affecting some
of the other important parameters. Iterative image reconstruction methods such
as the ordered subsets expectation maximization (OSEM) algorithm are able to
obtain some resolution recovery by using a realistic system response matrix
that includes all the relevant physical effects. Nevertheless, this resolution
recovery is often limited by reduced sampling in the projection space,
determined by the geometry of the detector. The goal of this work is to improve
the resolution beyond the detector size limit by increasing the sampling with
data-driven interpolated data. A maximum-likelihood estimation of the counts in
each virtual sub-line-of-response (subLOR) is obtained after a complete image
reconstruction, conserving the statistics of the initial data set. The new
estimation is used for the next complete reconstruction. The method typically
requires two or three of these full reconstructions (superiterations). We have
evaluated it with simulations and real acquisitions for the Argus and Super
Argus preclinical PET scanners manufactured by SMI, considering different types
of increased sampling. Quantitative measurements of recovery and resolution
evolution against noise per iteration for the standard OSEM and successive
superiterations show promising results. The procedure is able to reduce
significantly the impact of depth-of-interaction in large crystals, and to
improve the spatial resolution. The proposed method is quite general and it can
be applied to other scanners and configurations.
