Automatic Mismatch Correction and Motion Compensation for Free-breathing PET/CT

Abstract

In normal free-breathing PET/CT imaging protocol, a single CT image of the patient is obtained before the PET scan, which is inadequate for attenuation correction of the free-breathing PET data. The aim of this study is to design an automatic workflow to correct mismatch and reduce motion blurring of free-breathing PET and single-scan CT data. First, the PET data was divided into four equal-count respiratory frames using data-driven respiratory gating method. Modified attenuation map was used in during reconstruction to obtain mismatch-free gated PET images. Second, the respiratory phase of the original CT image was identified automatically using mutual information (MI) as the similarity metric. Two regions of interests (ROIs) near the lungs were segmented from CT image. The mutual information (MI) inside the ROIs of each gated PET reconstruction and the CT image was measured for all gates and the phase with highest MI value is identified as the correct respiratory phase of CT image. Third, using the respiratory phase of the CT image as the reference gate, MVFs from every other gate to the reference gate was estimated using a B-spline based multi-resolution image registration algorithm. Fourth, the original CT image with identified respiratory phase was transformed to every other frame using estimated MVFs to obtain attenuation maps for all respiratory frames. Finally, image reconstruction of gated PET data was repeated using phase-matched gated PET-CT data pairs. MVFs estimated in step 3 were applied to this set of gated PET images to obtain motion compensated PET image. This method was applied to clinical datasets using both F18-FDG and Ga68 -DOTA-NOC tracer. The proposed method successfully identified the respiratory phase of the CT image and generated phase-matched CT images for all respiratory phases of PET data. Final reconstructed PET data using the proposed method showed much-reduced mismatch artifacts and motion blurring, as well as improved statistics.