title: A nonrigid registration method for correcting brain deformation induced by tumor resection.
abstract: OBJECTIVE
  This paper presents a nonrigid registration method to align preoperative MRI with intraoperative MRI to compensate for brain deformation during tumor resection. This method extends traditional point-based nonrigid registration in two aspects: (1) allow the input data to be incomplete and (2) simulate the underlying deformation with a heterogeneous biomechanical model.
  
  
  METHODS
  The method formulates the registration as a three-variable (point correspondence, deformation field, and resection region) functional minimization problem, in which point correspondence is represented by a fuzzy assign matrix; Deformation field is represented by a piecewise linear function regularized by the strain energy of a heterogeneous biomechanical model; and resection region is represented by a maximal simply connected tetrahedral mesh. A nested expectation and maximization framework is developed to simultaneously resolve these three variables.
  
  
  RESULTS
  To evaluate this method, the authors conducted experiments on both synthetic data and clinical MRI data. The synthetic experiment confirmed their hypothesis that the removal of additional elements from the biomechanical model can improve the accuracy of the registration. The clinical MRI experiments on 25 patients showed that the proposed method outperforms the ITK implementation of a physics-based nonrigid registration method. The proposed method improves the accuracy by 2.88 mm on average when the error is measured by a robust Hausdorff distance metric on Canny edge points, and improves the accuracy by 1.56 mm on average when the error is measured by six anatomical points.
  
  
  CONCLUSIONS
  The proposed method can effectively correct brain deformation induced by tumor resection.
authors.affiliation: The Department of Computer Science, Old Dominion University, Norfolk, Virginia 23529.
authors.forename: Yixun
authors.initials: Y
authors.lastname: Liu
authors.affiliation: The Department of Neurosurgery, Huashan Hospital, Shanghai 200040, China.
authors.forename: Chengjun
authors.initials: C
authors.lastname: Yao
authors.affiliation: The Department of Computer Science, Old Dominion University, Norfolk, Virginia 23529.
authors.forename: Fotis
authors.initials: F
authors.lastname: Drakopoulos
authors.affiliation: The Department of Neurosurgery, Huashan Hospital, Shanghai 200040, China.
authors.forename: Jinsong
authors.initials: J
authors.lastname: Wu
authors.affiliation: The Department of Neurosurgery, Huashan Hospital, Shanghai 200040, China.
authors.forename: Liangfu
authors.initials: L
authors.lastname: Zhou
authors.affiliation: The Department of Computer Science, Old Dominion University, Norfolk, Virginia 23529.
authors.forename: Nikos
authors.initials: N
authors.lastname: Chrisochoides
mesh_terms: D000465:Algorithms; D001696:Biomechanical Phenomena; D001921:Brain / Q000473:pathology* / Q000601:surgery*; D001932:Brain Neoplasms / Q000473:pathology* / Q000601:surgery*; D003198:Computer Simulation; D005260:Female; D005910:Glioma / Q000473:pathology / Q000601:surgery; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D053783:Magnetic Resonance Imaging, Interventional / Q000379:methods*; D008297:Male; D008959:Models, Neurological; D019635:Neurosurgical Procedures / Q000379:methods