title: Correction of motion artifacts in cone-beam CT using a patient-specific respiratory motion model.
abstract: OBJECTIVE
  Respiratory motion adversely affects CBCT image quality and limits its localization accuracy for image-guided radiation treatment. Motion correction methods in CBCT have focused on the thorax because of its higher soft tissue contrast, whereas low-contrast tissue in abdomen remains a challenge. The authors report on a method to correct respiration-induced motion artifacts in 1 min CBCT scans that is applicable in both thorax and abdomen, using a motion model adapted to the patient from a respiration-correlated image set.
  
  
  METHODS
  Model adaptation consists of nonrigid image registration that maps each image to a reference image in the respiration-correlated set, followed by a principal component analysis to reduce errors in the nonrigid registration. The model parametrizes the deformation field in terms of observed surrogate (diaphragm or implanted marker) position and motion (inhalation or exhalation) between the images. In the thorax, the model is obtained from the same CBCT images that are to be motion-corrected, whereas in the abdomen, the model uses respiration-correlated CT (RCCT) images acquired prior to the treatment session. The CBCT acquisition is a single 360 degrees rotation lasting 1 min, while simultaneously recording patient breathing. The approximately 600 projection images are sorted into six (in thorax) or ten (in abdomen) subsets and reconstructed to obtain a set of low-quality respiration-correlated RC-CBCT images. Application of the motion model deforms each of the RC-CBCT images to a chosen reference image in the set; combining all images yields a single high-quality CBCT image with reduced blurring and motion artifacts. Repeated application of the model with different reference images produces a series of motion-corrected CBCT images over the respiration cycle, for determining the motion extent of the tumor and nearby organs at risk. The authors also investigate a simpler correction method, which does not use PCA and correlates motion state with respiration phase, thus assuming repeatable breathing patterns. Comparison of contrast-to-noise ratios of pixel intensities within anatomical structures relative to surrounding background tissue provides a quantitative assessment of relative organ visibility.
  
  
  RESULTS
  Evaluation in lung phantom, two patient cases in thorax and two in upper abdomen, shows that blurring and streaking artifacts are visibly reduced with motion correction. The boundaries of tumors in the thorax, liver, and kidneys are sharper and more discernible. Repeat application of the method in one thorax case, with reference images chosen at end expiration and end inspiration, indicates its feasibility for observing tumor motion extent. Phase-based motion correction without PCA reduces blurring less effectively; in addition, implanted markers appear broken up, indicating inconsistencies in the phase-based correction. In structures showing 1 cm or more motion excursion, PCA-based motion correction shows the highest contrast-to-noise ratios in the cases examined.
  
  
  CONCLUSIONS
  Motion correction of CBCT is feasible and yields observable improvement in the thorax and abdomen. The PCA-based model is an important component: First, by reducing deformation errors caused by the nonrigid registration and second, by relating deformation to surrogate position rather than phase, thus accommodating breathing pattern changes between imaging sessions. The accuracy of the method requires confirmation in further patient studies.
authors.affiliation: Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA.
authors.forename: Qinghui
authors.initials: Q
authors.lastname: Zhang
authors.forename: Yu-Chi
authors.initials: YC
authors.lastname: Hu
authors.forename: Fenghong
authors.initials: F
authors.lastname: Liu
authors.forename: Karyn
authors.initials: K
authors.lastname: Goodman
authors.forename: Kenneth E
authors.initials: KE
authors.lastname: Rosenzweig
authors.forename: Gig S
authors.initials: GS
authors.lastname: Mageras
mesh_terms: D000465:Algorithms; D016477:Artifacts; D003198:Computer Simulation; D054893:Cone-Beam Computed Tomography / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D009068:Movement / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011860:Radiography, Abdominal / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods*; D015203:Reproducibility of Results; D015656:Respiratory Mechanics; D012680:Sensitivity and Specificity