Evaluation of various deformable image registration algorithms for thoracic images

Noriyuki Kadoya, Yukio Fujita, Yoshiyuki Katsuta, Suguru Dobashi, Ken Takeda, Kazuma Kishi, Masaki Kubozono, Rei Umezawa, Toshiyuki Sugawara, Haruo Matsushita, Keiichi Jingu

Research output: Contribution to journalArticlepeer-review

75 Citations (Scopus)


We evaluated the accuracy of one commercially available and three publicly available deformable image registration (DIR) algorithms for thoracic four-dimensional (4D) computed tomography (CT) images. Five patients with esophagus cancer were studied. Datasets of the five patients were provided by DIR-lab (dir-lab.com) and consisted of thoracic 4D CT images and a coordinate list of anatomical landmarks that had been manually identified. Expert landmark correspondence was used for evaluating DIR spatial accuracy. First, the manually measured displacement vector field (mDVF) was obtained from the coordinate list of anatomical landmarks. Then the automatically calculated displacement vector field (aDVF) was calculated by using the following four DIR algorithms: B-spine implemented in Velocity AI (Velocity Medical, Atlanta, GA, USA), free-form deformation (FFD), Horn-Schunk optical flow (OF) and Demons in DIRART of MATLAB software. Registration error is defined as the difference between mDVF and aDVF. The mean 3D registration errors were 2.7 ± 0.8 mm for B-spline, 3.6 ± 1.0 mm for FFD, 2.4 ± 0.9 mm for OF and 2.4 ± 1.2 mm for Demons. The results showed that reasonable accuracy was achieved in B-spline, OF and Demons, and that these algorithms have the potential to be used for 4D dose calculation, automatic image segmentation and 4D CT ventilation imaging in patients with thoracic cancer. However, for all algorithms, the accuracy might be improved by using the optimized parameter setting. Furthermore, for B-spline in Velocity AI, the 3D registration error was small with displacements of less than ∼10 mm, indicating that this software may be useful in this range of displacements.

Original languageEnglish
Pages (from-to)175-182
Number of pages8
JournalJournal of Radiation Research
Issue number1
Publication statusPublished - 2014 Jan 1


  • adaptive radiotherapy
  • deformable image registration
  • image fusion
  • radiotherapy


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