title: A three-dimensional head-and-neck phantom for validation of multimodality deformable image registration for adaptive radiotherapy.
abstract: OBJECTIVE
  To develop a three-dimensional (3D) deformable head-and-neck (H&N) phantom with realistic tissue contrast for both kilovoltage (kV) and megavoltage (MV) imaging modalities and use it to objectively evaluate deformable image registration (DIR) algorithms.
  
  
  METHODS
  The phantom represents H&N patient anatomy. It is constructed from thermoplastic, which becomes pliable in boiling water, and hardened epoxy resin. Using a system of additives, the Hounsfield unit (HU) values of these materials were tuned to mimic anatomy for both kV and MV imaging. The phantom opens along a sagittal midsection to reveal radiotransparent markers, which were used to characterize the phantom deformation. The deformed and undeformed phantoms were scanned with kV and MV imaging modalities. Additionally, a calibration curve was created to change the HUs of the MV scans to be similar to kV HUs, (MC). The extracted ground-truth deformation was then compared to the results of two commercially available DIR algorithms, from Velocity Medical Solutions and mim software.
  
  
  RESULTS
  The phantom produced a 3D deformation, representing neck flexion, with a magnitude of up to 8 mm and was able to represent tissue HUs for both kV and MV imaging modalities. The two tested deformation algorithms yielded vastly different results. For kV-kV registration, mim produced mean and maximum errors of 1.8 and 11.5 mm, respectively. These same numbers for Velocity were 2.4 and 7.1 mm, respectively. For MV-MV, kV-MV, and kV-MC Velocity produced similar mean and maximum error values. mim, however, produced gross errors for all three of these scenarios, with maximum errors ranging from 33.4 to 41.6 mm.
  
  
  CONCLUSIONS
  The application of DIR across different imaging modalities is particularly difficult, due to differences in tissue HUs and the presence of imaging artifacts. For this reason, DIR algorithms must be validated specifically for this purpose. The developed H&N phantom is an effective tool for this purpose.
authors.affiliation: Department of Radiation Oncology, University of California San Francisco, San Francisco, California 94143-1708.
authors.forename: Kamal
authors.initials: K
authors.lastname: Singhrao
authors.affiliation: Department of Radiation Oncology, University of California San Francisco, San Francisco, California 94143-1708.
authors.forename: Neil
authors.initials: N
authors.lastname: Kirby
authors.affiliation: Department of Radiation Oncology, University of California San Francisco, San Francisco, California 94143-1708.
authors.forename: Jean
authors.initials: J
authors.lastname: Pouliot
mesh_terms: D000465:Algorithms; D055592:Biophysical Phenomena; D006258:Head and Neck Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology / Q000532:radiotherapy*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000706:statistics & numerical data; D008953:Models, Anatomic; D019047:Phantoms, Imaging / Q000706:statistics & numerical data; D010969:Plastics; D011857:Radiographic Image Interpretation, Computer-Assisted; D011880:Radiotherapy Planning, Computer-Assisted / Q000706:statistics & numerical data; D011882:Radiotherapy, High-Energy / Q000706:statistics & numerical data; D061089:Radiotherapy, Image-Guided / Q000706:statistics & numerical data; D014057:Tomography, X-Ray Computed / Q000706:statistics & numerical data
chemical_list: D010969:Plastics