title: Localization of deformable tumors from short-arc projections using Bayesian estimation.
abstract: OBJECTIVE
  The authors present a stochastic framework for radiotherapy patient positioning directly utilizing radiographic projections. This framework is developed to be robust against anatomical nonrigid deformations and to cope with challenging imaging scenarios, involving only a few cone beam CT projections from short arcs.
  
  
  METHODS
  Specifically, a Bayesian estimator (BE) is explicitly derived for the given scanning geometry. This estimator is compared to reference methods such as chamfer matching (CM) and the minimization of the median absolute error adapted as tools of robust image processing and statistics. In order to show the performance of the stochastic short-arc patient positioning method, a CIRS IMRT thorax phantom study is presented with movable markers and the utilization of an Elekta Synergy(®) XVI system. Furthermore, a clinical prostate CBCT scan of a Varian(®) On-Board Imager(®) system is utilized to investigate the robustness of the method for large variations of image quality (anterior-posterior vs lateral views).
  
  
  RESULTS
  The results show that the BE shifts reduce the initial setup error of up to 3 cm down to 3 mm at maximum for an imaging arc as short as 10° while CM achieves residual errors of 7 mm at maximum only for arcs longer than 40°. Furthermore, the BE can compensate robustly for low image qualities using several low quality projections simultaneously.
  
  
  CONCLUSIONS
  In conclusion, an estimation method for marker-based patient positioning for short imaging arcs is presented and shown to be robust and accurate for deformable anatomies.
authors.affiliation: Department of Radiation Oncology, Regensburg University Medical Center, Regensburg, Germany. wolfgang@hoegele.de
authors.forename: W
authors.initials: W
authors.lastname: Hoegele
authors.forename: P
authors.initials: P
authors.lastname: Zygmanski
authors.forename: B
authors.initials: B
authors.lastname: Dobler
authors.forename: M
authors.initials: M
authors.lastname: Kroiss
authors.forename: O
authors.initials: O
authors.lastname: Koelbl
authors.forename: R
authors.initials: R
authors.lastname: Loeschel
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008297:Male; D056888:Patient Positioning / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D020266:Radiotherapy, Conformal / Q000379:methods*; D061089:Radiotherapy, Image-Guided / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*