  Small animal PET scanners require high spatial resolution and good
sensitivity. To reconstruct high-resolution images in 3D-PET, iterative
methods, such as OSEM, are superior to analytical reconstruction algorithms,
although their high computational cost is still a serious drawback. The higher
performance of modern computers could make iterative image reconstruction fast
enough to be viable, provided we are able to deal with the large number of
probability coefficients for the system response matrix in high-resolution PET
scanners, which is a difficult task that prevents the algorithms from reaching
peak computing performance. Considering all possible axial and in-plane
symmetries, as well as certain quasi-symmetries, we have been able to reduce
the memory requirements to store the system response matrix (SRM) well below 1
GB, which allows us to keep the whole response matrix of the system inside RAM
of ordinary industry-standard computers, so that the reconstruction algorithm
can achieve near peak performance. The elements of the SRM are stored as cubic
spline profiles and matched to voxel size during reconstruction. In this way,
the advantages of on-the-fly calculation and of fully stored SRM are combined.
The on-the-fly part of the calculation (matching the profile functions to voxel
size) of the SRM accounts for 10 to 30% of the reconstruction time, depending
on the number of voxels chosen. We tested our approach with real data from a
commercial small animal PET scanner. The results (image quality and
reconstruction time) show that the proposed technique is a feasible solution.
