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.
['Miao|S|S|', 'Wang|Z J|ZJ|', 'Pan|L|L|', 'Butler|J|J|', 'Moran|G|G|', 'Liao|R|R|']
D000465:Algorithms D006801:Humans D021621:Imaging, Three-Dimensional D008279:Magnetic Resonance Imaging D009068:Movement D064847:Multimodal Imaging D049268:Positron-Emission Tomography D012119:Respiration
Respiratory motion Motion estimation MRI PET Image registration