Robust nonrigid registration to capture brain shift from intraoperative MRI

• Published in: IEEE transactions on medical imaging 2005-11, Vol.24 (11), p.1417-1427
• Main Authors: Clatz, O., Delingette, H., Talos, I.-F., Golby, A.J., Kikinis, R., Jolesz, F.A., Ayache, N., Warfield, S.K.
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Brain Neoplasms - pathology; Brain Neoplasms - physiopathology; Brain Neoplasms - surgery; Brain shift; Clustering algorithms; Computer Simulation; Elasticity; Finite element analysis; finite element model; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Intraoperative Care - methods; intraoperative magnetic resonance imaging; Iterative algorithms; Least squares approximation; Magnetic field measurement; Magnetic noise; Magnetic resonance; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Models, Biological; Motion; Neoplasms; Neuronavigation - methods; nonrigid registration; Registers; Reproducibility of Results; Robustness; Sensitivity and Specificity; Studies; Subtraction Technique; Surgery, Computer-Assisted - methods; User-Computer Interface
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2005.856734

We present a new algorithm to register 3-D preoperative magnetic resonance (MR) images to intraoperative MR images of the brain which have undergone brain shift. This algorithm relies on a robust estimation of the deformation from a sparse noisy set of measured displacements. We propose a new framework to compute the displacement field in an iterative process, allowing the solution to gradually move from an approximation formulation (minimizing the sum of a regularization term and a data error term) to an interpolation formulation (least square minimization of the data error term). An outlier rejection step is introduced in this gradual registration process using a weighted least trimmed squares approach, aiming at improving the robustness of the algorithm. We use a patient-specific model discretized with the finite element method in order to ensure a realistic mechanical behavior of the brain tissue. To meet the clinical time constraint, we parallelized the slowest step of the algorithm so that we can perform a full 3-D image registration in 35 s (including the image update time) on a heterogeneous cluster of 15 personal computers. The algorithm has been tested on six cases of brain tumor resection, presenting a brain shift of up to 14 mm. The results show a good ability to recover large displacements, and a limited decrease of accuracy near the tumor resection cavity.