title: Deformable image registration of liver with consideration of lung sliding motion.
abstract: OBJECTIVE
  A feature based deformable registration model with sliding transformation was developed in the upper abdominal region for liver cancer.
  
  
  METHODS
  A two-step thin-plate spline (bi-TPS) algorithm was implemented to deformably register the liver organ. The first TPS registration was performed to exclusively quantify the sliding displacement component. A manual segmentation of the thoracic and abdominal cavity was performed as a priori knowledge. Tissue feature points were automatically identified inside the segmented contour on the images. The scale invariant feature transform method was utilized to match feature points that served as landmarks for the subsequent TPS registration to derive the sliding displacement vector field. To a good approximation, only motion along superior/inferior (SI) direction of voxels on each slice was averaged to obtain the sliding displacement for each slice. A second TPS transformation, as the last step, was carried out to obtain the local deformation field. Manual identification of bifurcation on liver, together with the manual segmentation of liver organ, was employed as a "ground truth" for assessing the algorithm's performance.
  
  
  RESULTS
  The proposed two-step TPS was assessed with six liver patients. The average error of liver bifurcation between manual identification and calculation for these patients was less than 1.8 mm. The residual errors between manual contour and propagated contour of liver organ using the algorithm fell in the range between 2.1 and 2.8 mm. An index of Dice similarity coefficient (DSC) between manual contour and calculated contour for liver tumor was 93.6% compared with 71.2% from the conventional TPS calculation.
  
  
  CONCLUSIONS
  A high accuracy (∼2 mm) of the two-step feature based TPS registration algorithm was achievable for registering the liver organ. The discontinuous motion in the upper abdominal region was properly taken into consideration. Clinical implementation of the algorithm will find broad application in radiation therapy of liver cancer.
authors: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. yq.xie@siat.ac.cn
authors: Yaoqin
authors: Y
authors: Xie
authors: Ming
authors: M
authors: Chao
authors: Guanglei
authors: G
authors: Xiong
mesh_terms: D000465:Algorithms; D056973:Four-Dimensional Computed Tomography / Q000379:methods*; D006801:Humans; D008099:Liver / Q000000981:diagnostic imaging* / Q000473:pathology; D008113:Liver Neoplasms / Q000532:radiotherapy; D008168:Lung / Q000000981:diagnostic imaging* / Q000473:pathology; D015233:Models, Statistical; D009038:Motion; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012984:Software; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*