Enhanced 3D PET OSEM reconstruction using inter-update Metz filtering

We present an enhancement of the OSEM (ordered set expectation maximization ) algorithm for 3D PET reconstruction, which we call the inter-update Metz filtered OSEM (IMF-OSEM). The IMF-OSEM algorithm incorporates filtering act ion into the image updating process in order to improve the quality of the reconstruction. With this technique, the multiplicative correction image-or dinarily used to update image estimates in plain OSEM-is applied to a Metz- filtered version of the image estimate at certain intervals. In addition, we present a software implementation that employs several high -speed features to accelerate reconstruction. These features include, first ly, forward and back projection functions which make full use of symmetry a s well as a fast incremental computation technique. Secondly, the software has the capability of running in parallel mode on several processors. The p arallelization approach employed yields a significant speed-up, which is ne arly independent of the amount of data. Together, these features lead to re asonable reconstruction times even when using large image arrays and non-ax ially compressed projection data. The performance of IMF-OSEM was tested on phantom data acquired on the GE A dvance scanner. Our results demonstrate that an appropriate choice of Metz filter parameters can improve the contrast-noise balance of certain regions of interest relative to both plain and post-filtered OSEM, and to the GE c ommercial reprojection algorithm software.