Data-driven respiratory motion estimation and correction using TOF PET list-mode centroid of distribution

Abstract

Respiratory motion degrades PET image quality and is unavoidable. Respiratory gating and/or motion correction are usually performed to reduce the effect of respiratory motion. However, these methods generally require motion information from external devices. Several groups have proposed data-driven motion extraction methods by analyzing variation of counts, or center-of-mass in reconstructed dynamic images or in sinogram space. Now, with time-of-flight (TOF) PET, better localization of each annihilation point in three dimensions can be obtained. Here we propose a data-driven respiratory estimation method. Motion estimation is performed by computing centroids-of-distribution (COD) of all radioactive events in the field-of-view using list-mode PET data with TOF information, with time resolution of 100ms. We applied COD motion estimation in dynamic studies with 3 tracers targeted for the pancreas, lung fibrosis, and lung tumors, respectively. Firstly, COD traces were compared with measured motion from the Anzai system for all three studies. COD traces showed very good correlation with the Anzai data in pancreas studies. When comparing the trigger information extracted from COD and Anzai traces, about 90% of respiratory peak times could be identified to within 200ms. COD traces for the lung fibrosis study also visually showed a good correlation with Anzai traces. In the lung tumor study, COD did not provide reliable motion traces because of low contrast between moving organs and the background. Next, COD was used to gate a pancreas study, and the resulting respiratory-gated images were comparable to those based on Anzai data. Finally, using the same data, we performed the first completely data-driven continuous motion correction based on COD traces combined with 3D internal-external correlation. Initial qualitative results showed that COD-based continuous motion correction is visually comparable to Anzai-based motion correction, both showing substantial improvements in image quality.