title: Non-rigid deformation pipeline for compensation of superficial brain shift.
abstract: The correct visualization of anatomical structures is a critical component of neurosurgical navigation systems, to guide the surgeon to the areas of interest as well as to avoid brain damage. A major challenge for neuronavigation systems is the brain shift, or deformation of the exposed brain in comparison to preoperative Magnetic Resonance (MR) image sets. In this work paper, a non-rigid deformation pipeline is proposed for brain shift compensation of preoperative imaging datasets using superficial blood vessels as landmarks. The input was preoperative and intraoperative 3D image sets of superficial vessel centerlines. The intraoperative vessels (obtained using 3 Near-Infrared cameras) were registered and aligned with preoperative Magnetic Resonance Angiography vessel centerlines using manual interaction for the rigid transformation and, for the non-rigid transformation, the non-rigid point set registration method Coherent Point Drift. The rigid registration transforms the intraoperative points from the camera coordinate system to the preoperative MR coordinate system, and the non-rigid registration deals with local transformations in the MR coordinate system. Finally, the generation of a new deformed volume is achieved with the Thin-Plate Spline (TPS) method using as control points the matches in the MR coordinate system found in the previous step. The method was tested in a rabbit brain exposed via craniotomy, where deformations were produced by a balloon inserted into the brain. There was a good correlation between the real state of the brain and the deformed volume obtained using the pipeline. Maximum displacements were approximately 4.0 mm for the exposed brain alone, and 6.7 mm after balloon inflation.
authors.affiliation: Department of Neurosurgery, County Council of Ostergötland, Linköping, Sweden. filipe.marreiros@liu.se
authors.forename: Filipe M M
authors.initials: FM
authors.lastname: Marreiros
authors.affiliation: Department of Neurosurgery, County Council of Ostergötland, Linköping, Sweden.
authors.forename: Sandro
authors.initials: S
authors.lastname: Rossitti
authors.affiliation: Center for Medical Image Science and Visualization (CMIV), Linköping University, Sweden.
authors.forename: Chunliang
authors.initials: C
authors.lastname: Wang
authors.affiliation: Center for Medical Image Science and Visualization (CMIV), Linköping University, Sweden.
authors.forename: Örjan
authors.initials: Ö
authors.lastname: Smedby
mesh_terms: D000465:Algorithms; D000818:Animals; D016477:Artifacts; D001921:Brain / Q000098:blood supply* / Q000601:surgery*; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D018810:Magnetic Resonance Angiography / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D011817:Rabbits; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D025321:Surgery, Computer-Assisted / Q000379:methods*