Model‐based scatter correction for positron emission tomography (in German)

Abstract

Positron emission tomography (PET) images include a significant scatter component. The presence of the scatter manifests itself as a loss of spatial resolution and as an apparent migration of activity from hot to cold regions. Monte Carlo (MC) based approaches have proved to be very useful in simulating the image‐forming process in the PET scanner. However, fully 3D MC simulations are computationally too intensive to be applied in clinical routine. Consequently, many efforts have been undertaken to develop approximate scatter correction techniques for PET. Scatter correction is generally performed prior to image reconstruction using an appropriate model of the scatter process. These models require estimates of the correct emission and attenuation distribution in the imaged object. The problem is that these estimates are computed from measured data and, therefore, already contain scattered events. The purpose of this work was to overcome this problem by incorporating scatter characteristics directly into the process of iterative image reconstruction. This was achieved by an optimized implementation of the Single Scatter Simulation (SSS) algorithm resulting in a significant speed‐up of the scatter estimation procedure (from about 10 min to 30 s). The computationally improved SSS algorithm was then included in the forward projection step of maximum likelihood image reconstruction. Results obtained from phantom measurements demonstrate that this approach leads to a better estimation of the scatter component than can be obtained by a simple sequential data processing strategy.