Scatter to volume registration for model-free respiratory motion estimation from dynamic MRIs

Respiratory motion is one major complicating factor in many image acquisition applications and image-guided interventions. Existing respiratory motion estimation and compensation methods typically rely on breathing motion models learned from certain training data, and therefore may not be able to effectively handle intra-subject and/or inter-subject variations of respiratory motion. In this paper, we propose a respiratory motion compensation framework that directly recovers motion fields from sparsely spaced and efficiently acquired dynamic 2-D MRIs without using a learned respiratory motion model. We present a scatter-to-volume deformable registration algorithm to register dynamic 2-D MRIs with a static 3-D MRI to recover dense deformation fields. Practical considerations and approximations are provided to solve the scatter-to-volume registration problem efficiently. The performance of the proposed method was investigated on both synthetic and real MRI datasets, and the results showed significant improvements over the state-of-art respiratory motion modeling methods. We also demonstrated a potential application of the proposed method on MRI-based motion corrected PET imaging using hybrid PET/MRI.