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

S. Miao, Z. J. Wang, L. Pan, J. Butler, G. Moran, R. Liao

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
JournalComputerized Medical Imaging and Graphics
StateAccepted/In press - Aug 25 2015
Externally publishedYes


  • Image registration
  • Motion estimation
  • MRI
  • PET
  • Respiratory motion

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Health Informatics
  • Radiological and Ultrasound Technology
  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition


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