Robust nonrigid registration to capture brain shift from intraoperative MRI

Abstract

Abstract-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.Index Terms-Brain shift, finite element model, intraoperative magnetic resonance imaging, nonrigid registration.

On the unimportance of constitutive models in computing brain deformation for image-guided surgery

“…using finite deformations) finite element analysis was used as a solution method in this study. However, it should be noted that a number of research laboratories have reported computing deformations of the brain undergoing the shift using geometrically linear solution methods (Miga et al 1997, Hagemann et al 1999, Warfield et al 2000, Castellano-Smith et al 2001, Ferrant et al 2001, Ferrant et al 2002, Miga et al 2000, Clatz et al 2005, Dumpuri et al 2007). Such methods assume that the brain deformations are infinitesimally small.…”

How to image cell adhesion on soft polymers?

“…Though it alleviates symptoms, electrode implantation causes a direct inflammatory response and later a chronic response from the body. 3 One approach to reduce this response is to use softer support materials that can adapt better to soft tissue (Young's modulus 1 kPa to 180 kPa 4,5 ). This also improves close contact between devices and cells.…”

Grid-Enabled Software Environment for Enhanced Dynamic Data-Driven Visualization and Navigation During Image-Guided Neurosurgery

“…The registration method was first presented in [1], and subsequently evaluated in [4]. The computation consists of preoperative and intraoperative components.…”

“…The ICC segmentation [4] is prepared based on pre-operative imaging data. As the first intraoperative scan is available, preoperative data is rigidly (i.e., using translation and rotation) aligned with the patient's head position; 2. with the acquisition of the intraoperative image showing brain deformation, sparse displacement field is estimated from the intra-operative scan using blockmatching [1]. Its computation is based on the minimization of the correlation coefficient between regions, or blocks, of the pre-operative (aka floating) image and the real-time intra-operative (aka fixed) image; 3. the FEM model of the intra-cranial cavity with linear elastic constitutive equation is initialized with the mesh and sparse displacement field as the initial condition.…”

“…In the on-going collaboration between Brigham and Women's Hospital (BWH) in Boston, MA, and College of William and Mary (CWM) in Williamsburg, VA, we are studying how widely available commodity clusters and Grid resources can facilitate the timely delivery of registration results. We leverage our work from the state-of-the art registration method [1], and extensive experience with distributed processing and dynamic load balancing [2,3].…”