title: Scatter to volume registration for model-free respiratory motion estimation from dynamic MRIs.
abstract: 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.
authors.affiliation: Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; Medical Imaging Technology, Siemens Healthcare, Princeton, NJ 08540, USA. Electronic address: smiao@ece.ubc.ca.
authors.forename: S
authors.initials: S
authors.lastname: Miao
authors.affiliation: Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
authors.forename: Z J
authors.initials: ZJ
authors.lastname: Wang
authors.affiliation: Siemens Healthcare, Baltimore, MD 21205, USA.
authors.forename: L
authors.initials: L
authors.lastname: Pan
authors.affiliation: Lawson Health Research Institute, London, ON N6C 2R5, Canada.
authors.forename: J
authors.initials: J
authors.lastname: Butler
authors.affiliation: Siemens Canada, Oakville, ON L6H 0H6, Canada.
authors.forename: G
authors.initials: G
authors.lastname: Moran
authors.affiliation: Medical Imaging Technology, Siemens Healthcare, Princeton, NJ 08540, USA.
authors.forename: R
authors.initials: R
authors.lastname: Liao
mesh_terms: D000465:Algorithms; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009068:Movement; D064847:Multimodal Imaging / Q000379:methods*; D049268:Positron-Emission Tomography / Q000379:methods*; D012119:Respiration
keywords: Image registration; MRI; Motion estimation; PET; Respiratory motion