title: Technique for targeting arteriovenous malformations using frameless image-guided robotic radiosurgery.
abstract: OBJECTIVE
  To integrate three-dimensional (3D) digital rotation angiography (DRA) and two-dimensional (2D) digital subtraction angiography (DSA) imaging into a targeting methodology enabling comprehensive image-guided robotic radiosurgery of arteriovenous malformations (AVMs).
  
  
  METHODS
  DRA geometric integrity was evaluated by imaging a phantom with embedded markers. Dedicated DSA acquisition modes with preset C-arm positions were configured. The geometric reproducibility of the presets was determined, and its impact on localization accuracy was evaluated. An imaging protocol composed of anterior-posterior and lateral DSA series in combination with a DRA run without couch displacement between acquisitions was introduced. Software was developed for registration of DSA and DRA (2D-3D) images to correct for: (a) small misalignments of the C-arm with respect to the estimated geometry of the set positions and (b) potential patient motion between image series. Within the software, correlated navigation of registered DRA and DSA images was incorporated to localize AVMs within a 3D image coordinate space. Subsequent treatment planning and delivery followed a standard image-guided robotic radiosurgery process.
  
  
  RESULTS
  DRA spatial distortions were typically smaller than 0.3 mm throughout a 145-mm × 145-mm × 145-mm volume. With 2D-3D image registration, localization uncertainties resulting from the achievable reproducibility of the C-arm set positions could be reduced to about 0.2 mm. Overall system-related localization uncertainty within the DRA coordinate space was 0.4 mm. Image-guided frameless robotic radiosurgical treatments with this technique were initiated.
  
  
  CONCLUSIONS
  The integration of DRA and DSA into the process of nidus localization increases the confidence with which radiosurgical ablation of AVMs can be performed when using only an image-guided technique. Such an approach can increase patient comfort, decrease time pressure on clinical and technical staff, and possibly reduce the number of cerebral angiograms needed for a particular patient.
authors.affiliation: Radiation Oncology, Stanford University School of Medicine, Stanford, CA 94305-5847, USA. dimitre.hristov@stanford.edu
authors.forename: Dimitre
authors.initials: D
authors.lastname: Hristov
authors.forename: Lina
authors.initials: L
authors.lastname: Liu
authors.forename: John R
authors.initials: JR
authors.lastname: Adler
authors.forename: Iris C
authors.initials: IC
authors.lastname: Gibbs
authors.forename: Teri
authors.initials: T
authors.lastname: Moore
authors.forename: Marily
authors.initials: M
authors.lastname: Sarmiento
authors.forename: Steve D
authors.initials: SD
authors.lastname: Chang
authors.forename: Robert
authors.initials: R
authors.lastname: Dodd
authors.forename: Michael
authors.initials: M
authors.lastname: Marks
authors.forename: Huy M
authors.initials: HM
authors.lastname: Do
mesh_terms: D015901:Angiography, Digital Subtraction / Q000379:methods*; D005260:Female; D006801:Humans; D002538:Intracranial Arteriovenous Malformations / Q000000981:diagnostic imaging* / Q000601:surgery*; D009068:Movement; D019047:Phantoms, Imaging; D016634:Radiosurgery / Q000379:methods*; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods; D015203:Reproducibility of Results; D012371:Robotics; D012984:Software; D014057:Tomography, X-Ray Computed / Q000379:methods; D055815:Young Adult