High-resolution dynamic MR imaging of the thorax for respiratory motion correction of PET using groupwise manifold
Christian F Baumgartner1, Christoph Kolbitsch1, Daniel R Balfour1
1Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
Abstract:
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.
Insights
This study introduces a novel navigator-less method for respiratory motion correction in PET/MR imaging. The technique accurately reconstructs 3D PET volumes from 2D MR slices, improving disease diagnosis.
Area of Science:
- Medical Imaging
- Biophysics
- Computational Biology
Background:
- Respiratory motion degrades PET image quality, impacting disease diagnosis and staging.
- Current motion correction methods using 1D navigators fail to capture respiratory variability.
- Hybrid PET/MR systems offer combined molecular sensitivity and MR versatility, but lung imaging contrast is challenging.
Purpose of the Study:
- To develop a robust, navigator-less method for reconstructing dynamic 3D PET/MR volumes from 2D MR slice data.
- To accurately model and correct for respiratory motion variability in PET imaging.
- To improve the quality of PET images for enhanced disease diagnosis and staging.
Main Methods:
- Utilized manifold learning to extract low-dimensional representations from 2D MR slice data.
- Aligned extracted manifolds to achieve accurate slice matching with respect to respiratory position.
- Developed a navigator-less approach for dynamic 3D volume reconstruction from 2D slices.
Main Results:
- The proposed method significantly outperformed existing 1D navigator and image-based MR slice stacking techniques on synthetic data.
- Real-world data demonstrated plausible reconstructions and high consistency with the new method.
- Successfully applied the method for respiratory motion correction of simulated PET/MR data.
Conclusions:
- The novel manifold learning-based approach enables robust, navigator-less respiratory motion correction in PET/MR imaging.
- This technique improves 3D PET volume reconstruction accuracy by effectively utilizing 2D MR slice data.
- The method holds significant potential for advancing PET/MR applications in disease diagnosis and staging.
