Artifact model-based respiratory motion compensation (MoCo) for simultaneous PET/MR based on strongly undersampled radial MR data

We propose a new method for PET/MR respiratory motion compensation, which is based on strongly undersampled MR data. In our simulation study, we applied a 3D encoded radial stack-of-stars sampling scheme with 160 radial spokes per slice and an acquisition time of 38 s for MR data acquisition. Based on gated but strongly undersampled and thus streak artifact-contaminated 4D MR images, high-fidelity motion vector fields were estimated applying our newly-developed artifact model-based registration framework. Subsequently, MoCo 4D PET images of a simulated breathing thorax were reconstructed. Evaluation of eight artificial hot lesions in the lungs and upper abdomen showed a significant visual as well as a quantitative improvement in terms of SUVmean values, lesion size and localization for MoCo 4D PET images compared to 3D and 4D gated reconstructions especially for small lesion sizes.