title: Anatomical landmarks for point-matching registration in image-guided neurosurgery.
abstract: BACKGROUND
  Accurate patient to image registration is the core for successful image-guided neurosurgery. While skin adhesive markers (SMs) are widely used in point-matching registration, a proper implementation of anatomical landmarks (ALs) may overcome the inconvenience brought by the use of SMs.
  
  
  METHODS
  Using nine ALs, a set of three configurations of different combinations of them is proposed. These configurations are defined according to the required positioning of the patient's head during surgery and the resulting distribution of the expected target registration error (TRE). These configurations were first evaluated by simulation experiment using the data of 20 patients from two hospitals, and then testing the applicability of them in eight real clinical surgeries of neuronavigation.
  
  
  RESULTS
  The results of the simulation experiment showed that, by incorporating a fiducial registration error (FRE) of 3.5 mm measured in the clinical setting, the expected TRE in the whole skull was less than 2.5 mm, and the expected TRE in the whole brain was less than 1.75 mm when using all the nine ALs. A small TRE could also be achieved in the corresponding surgical field by using the other three configurations with less ALs. In the clinical experiment, the FLE ranges in the image and the patient space were 1.4-3.6 mm and 1.6-5.5 mm, respectively. The measured TRE and FRE were 3.1 ± 0.75 mm and 3.5 ± 0.17 mm, respectively.
  
  
  CONCLUSIONS
  The AL configurations proposed in this investigation provide sufficient registration accuracy and can help to avoid the disadvantages of SMs if used clinically.
authors.affiliation: Digital Medical Research Center of Shanghai Medical College, Fudan University, Shanghai, and Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention, Shanghai, China.
authors.forename: Akram I
authors.initials: AI
authors.lastname: Omara
authors.forename: Manning
authors.initials: M
authors.lastname: Wang
authors.forename: Yifeng
authors.initials: Y
authors.lastname: Fan
authors.forename: Zhijian
authors.initials: Z
authors.lastname: Song
mesh_terms: D000465:Algorithms; D001921:Brain / Q000601:surgery; D006257:Head / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D021621:Imaging, Three-Dimensional; D038361:Neuronavigation / Q000379:methods; D019635:Neurosurgical Procedures / Q000295:instrumentation / Q000379:methods*; D056888:Patient Positioning; D015203:Reproducibility of Results; D012867:Skin / Q000473:pathology; D025321:Surgery, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D014057:Tomography, X-Ray Computed
keywords: anatomical landmarks; image-guided neurosurgery; point-matching registration; target registration error