Capturing Brain Deformation

Warfield, Simon K.; Talos, Florin; Kemper, Corey; O’Donnell, Lauren J.; Westin, Carl‐Fredrik; Wells, William M.; Black, Peter McL.; Jólesz, Ferenc A.; Kikinis, Ron

doi:10.1007/3-540-45015-7_20

Cited by 4 publications

(4 citation statements)

References 27 publications

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“…Reduction of intracranial pressure, decreased reliance on steroids and improvement of functional status are the advantages of such a therapeutic policy [17]. To minimize the risk of postoperative, permanent neurological deficits, which are crucial for the quality of a patient's life after aggressive surgery, reliable knowledge of the location of functional centers is of the highest value.…”

Section: Discussionmentioning

confidence: 99%

Cortical mapping by functional magnetic resonance imaging in patients with brain tumors

et al. 2004

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The aim of our study was to establish the effectiveness of the functional MRI (fMRI) technique in comparison with intraoperative cortical stimulation (ICS) in planning cortex-saving neurosurgical interventions. The combination of sensory and motor stimulation during fMRI experiments was used to improve the exactness of central sulcus localization. The study subjects were 30 volunteers and 33 patients with brain tumors in the rolandic area. Detailed topographical relations of activated areas in fMRI and intraoperative techniques were compared. The agreement in the location defined by the two methods for motor centers was found to be 84%; for sensory centers it was 83%. When both kinds of activation are taken into account this agreement increases to 98%. A significant relation was found between fMRI and ICS for the agreement of the distance both for motor and sensory centers (p=0.0021-0.0024). Also a strong dependence was found between the agreement of the location and the agreement of the distance for both kinds of stimulation. The spatial correlation between fMRI and ICS methods for the sensorimotor cortex is very high. fMRI combining functional and structural information is very helpful for preoperative neurosurgical planning. The sensitivity of the fMRI technique in brain mapping increases when using both motor and sensory paradigms in the same patient.

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Section: Discussionmentioning

confidence: 99%

Cortical mapping by functional magnetic resonance imaging in patients with brain tumors

et al. 2004

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“…Based on the elastic property, the general elasticity equilibrium equation can be written as follows [6][7][8][9]:…”

Section: Proposed Finite-element Modelmentioning

confidence: 99%

“…One of the important factors in MIS is how to quantify the deformation induced by surgical tools. The finite-element method (FEM) has been used in MIS of brain [1][2][3][4][5][6][7][8][9][10][11] and liver [12]. Traditionally, the FEM is applied over the entire volume, despite the fact that the surgical tool (e.g., the endoscope in minimally invasive brain surgery) is applied only over a small and well-specified region of the brain.…”

Section: Introductionmentioning

confidence: 99%

Intensity and region-of-interest-based finite-element modeling of brain deformation

Shi

Farag

2004

International Congress Series

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“…Here we are interested in simulation frameworks for medical imaging, particularly in the context of reducing the computational time and cost. Examples are Dawant et al (1999), Ferrant et al (2001), Miga et al (1998), Warfield et al (2002Warfield et al ( , 2003 and Cotin et al (1999). Biomechanical simulations of tissue deformations usually start with obtaining a segmentation of the target geometry from a medical image which is then used to reconstruct a representation of the target geometry's boundary surface.…”

Section: Introductionmentioning

confidence: 99%

A robust framework for soft tissue simulations with application to modeling brain tumor mass effect in 3D MR images

et al. 2007

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We present a framework for black-box and flexible simulation of soft tissue deformation for medical imaging and surgical planning applications. Our main motivation in the present work is to develop robust algorithms that allow batch processing for registration of brains with tumors to statistical atlases of normal brains and construction of brain tumor atlases. We describe a fully Eulerian formulation able to handle large deformations effortlessly, with a level-set-based approach for evolving fronts. We use a regular grid-fictitious domain method approach, in which we approximate coefficient discontinuities, distributed forces and boundary conditions. This approach circumvents the need for unstructured mesh generation, which is often a bottleneck in the modeling and simulation pipeline. Our framework employs penalty approaches to impose boundary conditions and uses a matrix-free implementation coupled with a multigrid-accelerated Krylov solver. The overall scheme results in a scalable method with minimal storage requirements and optimal algorithmic complexity. We illustrate the potential of our framework to simulate realistic brain tumor mass effects at reduced computational cost, for aiding the registration process towards the construction of brain tumor atlases.

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Capturing Brain Deformation

Cited by 4 publications

References 27 publications

Cortical mapping by functional magnetic resonance imaging in patients with brain tumors

Cortical mapping by functional magnetic resonance imaging in patients with brain tumors

Intensity and region-of-interest-based finite-element modeling of brain deformation

A robust framework for soft tissue simulations with application to modeling brain tumor mass effect in 3D MR images

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