title: Determining the position of a patient reference from C-Arm views for image guided navigation.
abstract: OBJECTIVE
  Image Guided Surgery (IGS) navigation systems may acquire the position of an instrument relative to the patient with an infrared light-based stereo tracking camera. The measured instrument position is then transformed from the tracking coordinate system to the coordinate system of the intraoperatively acquired medical images.
  
  
  METHODS
  A robust and practical automatic method was developed to determine the coordinate transformation from the tracking device to intraoperatively acquired images. The method works with a patient reference device that contains both fluoroscopic markers and tracking markers in a defined geometric arrangement which is fixed on the patient. As precondition the patient reference must be acquired by at least two fluoroscopic images. From the positions of the fluoroscopic markers in these images, the location and orientation is determined and the tracking-to-image transformation is computed. 3D localization of the fluoroscopic reference markers is determined by a three-step process: marker detection, correspondence calculation and triangulation. These steps are implemented in an automatic and robust manner using a new correspondence calculation method.
  
  
  RESULTS
  The improved SVD matching method was evaluated experimentally using both synthetic point sets and fluoroscopic marker sets detected from 66 image pairs from a bone and soft tissue phantom acquired by a fluoroscopic c-arm system (Siemens Artis zee Biplane system). For the ideal point sets without outliers 100% of the correspondences were correct. For the noised point sets with up to 20% rogue points 84% correspondence were correct. For lateral translations between the directions of acquisition, the normalized SVD matching method is shown to be as robust as the original approach proposed by Scott and Longuet-Higgins [15]. For other translations, rotations, scaling and shear transformations our method is more robust. The accuracy of the 3D reconstruction approach was also evaluated with a patient phantom. The experiment was repeated with projection images having variant C-arm angulations from 10° to 130°. The results showed that the mean 3D error of the reconstructed markers was 0.36 mm with a standard deviation of 0.096 mm.
  
  
  CONCLUSIONS
  The 3D reconstruction method enables an effective tool to relate a tracking system to a FD-CT imaging system and provide adequate accuracy for most navigation applications.
authors.affiliation: CAS innovations GmbH & Co. KG., Am Heusteg 47, 91056, Erlangen, Germany. wei.wei@cas-innovations.de
authors.forename: Wei
authors.initials: W
authors.lastname: Wei
authors.forename: Nikolaus
authors.initials: N
authors.lastname: Schön
authors.forename: Tim
authors.initials: T
authors.lastname: Dannenmann
authors.forename: Ralf
authors.initials: R
authors.lastname: Petzold
mesh_terms: D000465:Algorithms; D005471:Fluoroscopy / Q000295:instrumentation; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000295:instrumentation; D055095:Optics and Photonics / Q000295:instrumentation*; D056888:Patient Positioning; D019047:Phantoms, Imaging; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D025321:Surgery, Computer-Assisted / Q000295:instrumentation*; D013525:Surgical Instruments; D015898:Tomography Scanners, X-Ray Computed; D014057:Tomography, X-Ray Computed