MR-based attenuation map re-alignment and motion correction in simultaneous brain MR-PET imaging

Abstract

Head movement is a major issue in dynamic PET imaging. A simultaneous MR-PET scanner is capable of acquiring both MR and PET data concurrently, which enables opportunities to use MR information for PET motion correction. Here we propose an MR-based method to detect head motion and to correct motion artefacts during PET image reconstruction. The method is based on co-registration of multiple MR contrasts to extract motion parameters. The motion parameters are then used to guide the Multiple Acquisition Frame (MAF) algorithm to bin the PET list-mode data into multiple frames whenever significant motion occurs. Furthermore, motion parameters are used to re-align the PET attenuation u-map to each frame prior to the image reconstruction. Finally, PET images are reconstructed for each frame and combined to produce a final image. Using both phantom and in-vivo human data, we show that this method can significantly increase image quality and reduce motion artefacts.