title: Self-adapting cyclic registration for motion-compensated cone-beam CT in image-guided radiation therapy.
abstract: OBJECTIVE
  In image-guided radiation therapy an additional kV imaging system next to the linear particle accelerator provides information for an accurate patient positioning. However, the acquisition time of the system is much longer than the patient's breathing cycle due to the low gantry rotation speed. Our purpose is a cyclic registration in the context of motion-compensated image reconstruction that provides high quality respiratory-correlated 4D volumes for on-board flat panel detector cone-beam CT scans.
  
  
  METHODS
  Based on the small motion assumption, widely used within registration algorithms, a strategy is developed for motion estimation. In this strategy temporal restrictions are incorporated, for example, the cyclic motion patterns of respiration. The resultant cyclic registration method is to show less sensitivity on image artifacts, in particular on artifacts due to projection data sparsification. Using a new cyclic registration method a motion estimation is performed on respiratory-correlated reconstructions, and the obtained motion vector fields are used for motion compensation.
  
  
  RESULTS
  The proposed cyclic registration is evaluated in the context of motion-compensated image reconstruction using simulated data and patient data. Motion artifacts of 3D standard reconstructions can be significantly reduced by the resulting cyclic motion compensation. The method outperforms the respiratory-correlated reconstructions regarding sparse-view artifacts and maintains the high temporal resolution at the same time. Image artifacts show only minor to almost no effect on the motion estimation using the cyclic registration.
  
  
  CONCLUSIONS
  The cyclic motion compensation approach provides respiratory-correlated volumes with high image quality. The cyclic motion estimation is of such low sensitivity to sparse-view artifacts, that it is capable to determine high quality motion vector fields based on reconstructions of low sampled data.
authors.affiliation: Institute of Medical Physics, Friedrich-Alexander-University, Erlangen, Germany. marcus.brehm@dkfz.de
authors.forename: Marcus
authors.initials: M
authors.lastname: Brehm
authors.forename: Pascal
authors.initials: P
authors.lastname: Paysan
authors.forename: Markus
authors.initials: M
authors.lastname: Oelhafen
authors.forename: Patrik
authors.initials: P
authors.lastname: Kunz
authors.forename: Marc
authors.initials: M
authors.lastname: Kachelrieß
mesh_terms: D000465:Algorithms; D016477:Artifacts; D054893:Cone-Beam Computed Tomography / Q000379:methods*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D009038:Motion; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D061089:Radiotherapy, Image-Guided / Q000379:methods*; D015203:Reproducibility of Results; D055425:Respiratory-Gated Imaging Techniques / Q000379:methods*; D012680:Sensitivity and Specificity; D013382:Subtraction Technique