title: A method for generating large datasets of organ geometries for radiotherapy treatment planning studies.
abstract: BACKGROUND
  With the rapidly increasing application of adaptive radiotherapy, large datasets of organ geometries based on the patient's anatomy are desired to support clinical application or research work, such as image segmentation, re-planning, and organ deformation analysis. Sometimes only limited datasets are available in clinical practice. In this study, we propose a new method to generate large datasets of organ geometries to be utilized in adaptive radiotherapy.
  
  
  METHODS
  Given a training dataset of organ shapes derived from daily cone-beam CT, we align them into a common coordinate frame and select one of the training surfaces as reference surface. A statistical shape model of organs was constructed, based on the establishment of point correspondence between surfaces and non-uniform rational B-spline (NURBS) representation. A principal component analysis is performed on the sampled surface points to capture the major variation modes of each organ.
  
  
  RESULTS
  A set of principal components and their respective coefficients, which represent organ surface deformation, were obtained, and a statistical analysis of the coefficients was performed. New sets of statistically equivalent coefficients can be constructed and assigned to the principal components, resulting in a larger geometry dataset for the patient's organs.
  
  
  CONCLUSIONS
  These generated organ geometries are realistic and statistically representative.
authors.affiliation: Department of Radiation Oncology, Cancer Center,Research Institute of Surgery, Daping Hospital, Third Military Medical University, China ; Department of Radiation Oncology, University of California, San Diego, USA ; College of Bioengineering, Chongqing University, China.
authors.forename: Nan
authors.initials: N
authors.lastname: Hu
authors.affiliation: Department of Radiation Oncology, University of California, San Diego, USA.
authors.forename: Laura
authors.initials: L
authors.lastname: Cerviño
authors.affiliation: Department of Radiology, Duke University, USA.
authors.forename: Paul
authors.initials: P
authors.lastname: Segars
authors.affiliation: Department of Radiation Oncology, University of California, San Diego, USA.
authors.forename: John
authors.initials: J
authors.lastname: Lewis
authors.affiliation: Department of Radiology, Duke University, USA.
authors.forename: Jinlu
authors.initials: J
authors.lastname: Shan
authors.affiliation: Department of Radiation Oncology, University of California, San Diego, USA.
authors.forename: Steve
authors.initials: S
authors.lastname: Jiang
authors.affiliation: College of Bioengineering, Chongqing University, China.
authors.forename: Xiaolin
authors.initials: X
authors.lastname: Zheng
authors.affiliation: Department of Radiation Oncology, Cancer Center,Research Institute of Surgery, Daping Hospital, Third Military Medical University, China.
authors.forename: Ge
authors.initials: G
authors.lastname: Wang
keywords: adaptive radiotherapy; new geometries; non-uniform rational B-spline technique; statistical shape model