Real-Time Biomechanical Simulation of Volumetric Brain Deformation for Image Guided Neurosurgery

Abstract

We aimed to study the performance of a parallel implementation of an intraoperative nonrigid registration algorithm that accurately simulates the biomechanical properties of the brain and its deformations during surgery. The algorithm was designed to allow for improved surgical navigation and quantitative monitoring of treatment progress in order to improve the surgical outcome and to reduce the time required in the operating room. We have applied the algorithm to two neurosurgery cases with promising results.High performance computing is a key enabling technology that allows the biomechanical simulation to be executed quickly enough for the algorithm to be practical. Our parallel implementation was evaluated on a symmetric multi-processor and two clusters and exhibited similar performance characteristics on each. The implementation was sufficiently fast to be used in the operating room during a neurosurgery procedure. It allowed a three-dimensional volumetric deformation to be simulated in less than ten seconds.

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.…”

“…the brain) deformations must be taken into account. Since the late 1990s significant research effort has been directed towards the prediction of such deformations using biomechanical models (Miga et al 1997, Hagemann et al 1999, Warfield et al 2000, Ferrant et al 2001, Ferrant et al 2002, Castellano-Smith et al 2001, Xu and Nowinski 2001, Miga et al 2000, Wittek et al 2005, Dumpuri et al 2007). Typically, in such models, the Finite Element (FE) method (e.g.…”

Landmark-Guided Surface Matching and Volumetric Warping for Improved Prostate Biopsy Targeting and Guidance

“…In particular, we have investigated solving the system of linear equations on a set of parallel architectures. See [20] for details. We have recently experimented with both an inexpensive workstation, a Dell Precision 650n with dual 3.0 GHz Intel Xeon CPUs running Linux, and a cluster of such workstations connected by 100Mbps Fast Ethernet.…”

Design and Implementation of Parallel Nonrigid Image Registration Using Off-the-Shelf Supercomputers

“…To realize fast nonrigid registration, high performance computing approaches have been employed in recent works [1,2,3]. In [1], a shared memory multiprocessors reduced the registration time for brain MR images of 256×256×100 voxels from one hour on one processor to approximately a hundred seconds on 64 processors.…”

“…In [1], a shared memory multiprocessors reduced the registration time for brain MR images of 256×256×100 voxels from one hour on one processor to approximately a hundred seconds on 64 processors. On the other hand, [2,3] employed clusters [4], or a distributed memory multiprocessors, which has advantages of cost effectiveness and extensibility compared to shared memory multiprocessors. In [2], a cluster of workstations realized intraoperative registration of segmented brain images within 10 seconds.…”