High-resolution dynamic MR imaging of the thorax for respiratory motion correction of PET using groupwise manifold alignment.

Respiratory motion is a complicating factor in PET imaging as it leads to blurring of the reconstructed images which adversely affects disease diagnosis and staging. Existing motion correction techniques are often based on 1D navigators which cannot capture the inter- and intra-cycle variabilities that may occur in respiration. MR imaging is an attractive modality for estimating such motion more accurately, and the recent emergence of hybrid PET/MR systems allows the combination of the high molecular sensitivity of PET with the versatility of MR. However, current MR imaging techniques cannot achieve good image contrast inside the lungs in 3D. 2D slices, on the other hand, have excellent contrast properties inside the lungs due to the in-flow of previously unexcited blood, but lack the coverage of 3D volumes. In this work we propose an approach for the robust, navigator-less reconstruction of dynamic 3D volumes from 2D slice data. Our technique relies on the fact that data acquired at different slice positions have similar low-dimensional representations which can be extracted using manifold learning. By aligning these manifolds we are able to obtain accurate matchings of slices with regard to respiratory position. The approach naturally models all respiratory variabilities. We compare our method against two recently proposed MR slice stacking methods for the correction of PET data: a technique based on a 1D pencil beam navigator, and an image-based technique. On synthetic data with a known ground truth our proposed technique produces significantly better reconstructions than all other examined techniques. On real data without a known ground truth the method gives the most plausible reconstructions and high consistency of reconstruction. Lastly, we demonstrate how our method can be applied for the respiratory motion correction of simulated PET/MR data.