title: Fiducial-based registration with a touchable region model.
abstract: OBJECTIVE
  Image-guided surgery requires registration between an image coordinate system and an intraoperative coordinate system that is typically referenced to a tracking device. In fiducial-based registration methods, this is achieved by localizing points (fiducials) in each coordinate system. Often, both localizations are performed manually, first by picking a fiducial point in the image and then by using a hand-held tracked pointer to physically touch the corresponding fiducial on the patient. These manual procedures introduce localization error that is user-dependent and can significantly decrease registration accuracy. Thus, there is a need for a registration method that is tolerant of imprecise fiducial localization in the preoperative and intraoperative phases.
  
  
  METHODS
  We propose the iterative closest touchable point (ICTP) registration framework, which uses model-based localization and a touchable region model. This method consists of three stages: (1) fiducial marker localization in image space, using a fiducial marker model, (2) initial registration with paired-point registration, and (3) fine registration based on the iterative closest point method.
  
  
  RESULTS
  We perform phantom experiments with a fiducial marker design that is commonly used in neurosurgery. The results demonstrate that ICTP can provide accuracy improvements compared to the standard paired-point registration method that is widely used for surgical navigation and surgical robot systems, especially in cases where the surgeon introduces large localization errors.
  
  
  CONCLUSIONS
  The results demonstrate that the proposed method can reduce the effect of the surgeon's localization performance on the accuracy of registration, thereby producing more consistent and less user-dependent registration outcomes.
authors: Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA. sungminkim@jhu.edu.
authors: Sungmin
authors: S
authors: Kim
authors: Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA.
authors: Peter
authors: P
authors: Kazanzides
mesh_terms: D000465:Algorithms; D057918:Fiducial Markers; D006801:Humans; D008953:Models, Anatomic; D008962:Models, Theoretical; D038361:Neuronavigation / Q000379:methods; D019635:Neurosurgical Procedures / Q000379:methods*; D019047:Phantoms, Imaging; D025321:Surgery, Computer-Assisted / Q000379:methods*
keywords: Fiducial marker; Registration; Surgical navigation; Surgical robot