Respiratory Motion Correction in PET Imaging: Comparative Analysis of External Device and Data-driven Gating Approaches

Sammanfattning: Positron Emission Tomography (PET) is pivotal in medical imaging but is prone to artifactsfrom physiological movements, notably respiration. These motion artifacts both degradeimage quality and compromise precise attenuation correction. To counteract this, gatingstrategies partition PET data in synchronization with respiratory cycles, ensuring each gatenearly represents a static phase. Additionally, a 3D deep learning image registration modelcan be used for inter-gate motion correction, maximizing the use of the full acquired data. Thisstudy aimed to implement and evaluate two gating strategies: an external device-based approachand a data-driven centroid-of-distribution (COD) trace algorithm, and assess their impact on theperformance of the registration model. Analysis of clinical data from four subjects indicated thatthe external device approach outperformed its data-driven counterpart, which faced challengesin real-patient settings. Post motion compensation, both methods achieved results comparableto state-of-the-art reconstructions, suggesting the deep learning model addressed some data-driven method limitations. However, the motion corrected outputs did not exhibit significantimprovements in image quality over state-of-the-art standards.