Efficient rendering of regions of response in list-mode reconstruction for PET

We have developed a new algorithm for fast region of response rendering in Positron Emission Tomography (PET). The algorithm can be applied efficiently to Expectation Maximization (EM) techniques that include per-event probability distributions with shapes more complex than symmetric tubes of response. The algorithm is generic for any system geometry and draws the scalar field associated to a generic input kernel with a constant cut-off threshold independent from the actual kernel shape. In the current version it has been tested for reconstructing List-Mode (LM) data simulated with GATE on the rPET small animal scanner and used with a 2D Gaussian kernel specifically designed for the rPET. EM parallelization has been achieved at per-event level on a 8 core dual CPU. Results show that the new algorithm allows for LM reconstruction in low times with complex kernel functions of the relative position of the voxel with respect to the line of response.