Spatially variant resolution modelling using redistributed lines-of-response and the image space reconstruction algorithm

A spatially variant resolution modelling technique for PET image reconstruction is presented which models the physical processes of the measurement during the iterative reconstruction. This is achieved by redistributing the line-of-response endpoints according to derived probability density functions describing the detector response function and photon acollinearity. When applying this technique it is shown that, to avoid mathematical inconsistencies and reconstruction artefacts, MLEM cannot be used for the reconstruction. The ISRA algorithm, after being adapted to a list-mode based implementation, is used instead since its structure is well-suited to this application. The Redistribution technique is shown to produce superior resolution recovery in off-centre phantom reconstructions than the standard stationary image-space Gaussian convolution approach, and it only requires approximately 35% more computation time.