title: Fast rigid registration of pre-operative magnetic resonance images to intra-operative ultrasound for neurosurgery based on high confidence gradient orientations.
abstract: OBJECTIVE
  We present a novel approach for the registration of pre-operative magnetic resonance images to intra-operative ultrasound images for the context of image-guided neurosurgery.
  
  
  METHODS
  Our technique relies on the maximization of gradient orientation alignment in a reduced set of high confidence locations of interest and allows for fast, accurate, and robust registration. Performance is compared with multiple state-of-the-art techniques including conventional intensity-based multi-modal registration strategies, as well as other context-specific approaches. All methods were evaluated on fourteen clinical neurosurgical cases with brain tumors, including low-grade and high-grade gliomas, from the publicly available MNI BITE dataset. Registration accuracy of each method is evaluated as the mean distance between homologous landmarks identified by two or three experts. We provide an analysis of the landmarks used and expose some of the limitations in validation brought forward by expert disagreement and uncertainty in identifying corresponding points.
  
  
  RESULTS
  The proposed approach yields a mean error of 2.57 mm across all cases (the smallest among all evaluated techniques). Additionally, it is the only evaluated technique that resolves all cases with a mean distance of less than 1 mm larger than the theoretical minimal mean distance when using a rigid transformation.
  
  
  CONCLUSIONS
  Finally, our proposed method provides reduced processing times with an average registration time of 0.76 s in a GPU-based implementation, thereby facilitating its integration into the operating room.
authors.affiliation: Centre for Intelligent Machines, McGill University, Montreal, QC, H3A 0E9, Canada. dante@cim.mcgill.ca
authors.forename: Dante
authors.initials: D
authors.lastname: De Nigris
authors.forename: D Louis
authors.initials: DL
authors.lastname: Collins
authors.forename: Tal
authors.initials: T
authors.lastname: Arbel
mesh_terms: D000465:Algorithms; D001932:Brain Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology / Q000601:surgery*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D016343:Monitoring, Intraoperative / Q000379:methods; D019635:Neurosurgical Procedures / Q000379:methods*; D015203:Reproducibility of Results; D014463:Ultrasonography