An Anisotropic Material Model for Image Guided Neurosurgery

Kemper, Corey; Talos, Ion-Florin; Golby, Alexandra J.; Black, Peter McL.; Kikinis, Ron; Grimson, W. Eric L.; Warfield, Simon K.

doi:10.1007/978-3-540-30136-3_34

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…This type of application would most likely also require a more detailed biomechanical model. Such models are currently in development [1], [13], [22], [23], [32], [36], [37], [55], [65] and, as shown in [18], could easily be applied in conjunction with the game theoretic surface detection. These solution possibilities can be tested in phantom experiments or on ex vivo tissue.…”

Section: Discussionmentioning

confidence: 99%

Image-Guided Intraoperative Cortical Deformation Recovery Using Game Theory: Application to Neocortical Epilepsy Surgery

DeLorenzo

Papademetris

Staib

et al. 2010

IEEE Trans. Med. Imaging

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During neurosurgery, nonrigid brain deformation prevents preoperatively-acquired images from accurately depicting the intraoperative brain. Stereo vision systems can be used to track intraoperative cortical surface deformation and update preoperative brain images in conjunction with a biomechanical model. However, these stereo systems are often plagued with calibration error, which can corrupt the deformation estimation. In order to decouple the effects of camera calibration from the surface deformation estimation, a framework that can solve for disparate and often competing variables is needed. Game theory, which was developed to handle decision making in this type of competitive environment, has been applied to various fields from economics to biology. In this paper, game theory is applied to cortical surface tracking during neocortical epilepsy surgery and used to infer information about the physical processes of brain surface deformation and image acquisition. The method is successfully applied to eight in vivo cases, resulting in an 81% decrease in mean surface displacement error. This includes a case in which some of the initial camera calibration parameters had errors of 70%. Additionally, the advantages of using a game theoretic approach in neocortical epilepsy surgery are clearly demonstrated in its robustness to initial conditions.

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Section: Discussionmentioning

confidence: 99%

Image-Guided Intraoperative Cortical Deformation Recovery Using Game Theory: Application to Neocortical Epilepsy Surgery

DeLorenzo

Papademetris

Staib

et al. 2010

IEEE Trans. Med. Imaging

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“…Nonlinear [53] or anisotropic [26] brain models have the potential to incrementally improve LEM deformation calculations in some areas of the brain; however, they require more computation, brain parameter estimation or additional imaging, such as Diffusion Tensor Imaging (DTI). Additionally, the comparison between these and homogeneous linear models has been limited and generally based on small deformations.…”

Section: Methodsmentioning

confidence: 99%

Volumetric Intraoperative Brain Deformation Compensation: Model Development and Phantom Validation

DeLorenzo

Papademetris

Staib

et al. 2012

IEEE Trans. Med. Imaging

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During neurosurgery, nonrigid brain deformation may affect the reliability of tissue localization based on preoperative images. To provide accurate surgical guidance in these cases, preoperative images must be updated to reflect the intraoperative brain. This can be accomplished by warping these preoperative images using a biomechanical model. Due to the possible complexity of this deformation, intraoperative information is often required to guide the model solution. In this paper, a linear elastic model of the brain is developed to infer volumetric brain deformation associated with measured intraoperative cortical surface displacement. The developed model relies on known material properties of brain tissue, and does not require further knowledge about intraoperative conditions. To provide an initial estimation of volumetric model accuracy, as well as determine the model’s sensitivity to the specified material parameters and surface displacements, a realistic brain phantom was developed. Phantom results indicate that the linear elastic model significantly reduced localization error due to brain shift, from >16 mm to under 5 mm, on average. In addition, though in vivo quantitative validation is necessary, preliminary application of this approach to images acquired during neocortical epilepsy cases confirms the feasibility of applying the developed model to in vivo data.

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“…By incorporating additional constraints acquired preoperatively, the recovered forces can be applied to the finite element model to estimate the entire displacement field. Kemper et al [64] used a homogeneous, isotropic, linear elastic FEM. It was extended to take into account anisotropy and hence the underlying white matter structure, derived in practice from diffusion tensor MRI images.…”

Section: Image Constrained Biomechanical Modellingmentioning

confidence: 99%

In vivo and in situ image guidance and modelling in robotic assisted surgery

Lee

Huntbatch

Pratt

et al. 2010

Proceedings of the Institution of Mechanical Engineers, Part C:

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The increase in the use of robotic assisted surgery has provided benefits to both the patient and the surgeon. There are possibilities for training and surgical planning and guidance within this framework but work is needed for this to reach a patient-specific level. Medical images obtained prior to surgery are used clinically to plan operations and there has been much research in their use to build models, giving further knowledge of tissue and organ structures. The incorporation of this data into the surgical environment is a recent development, assisting the surgeon with orientation information, but motion due to respiration or changes to morphology due to intervention begets a need for the incorporation of real-time imaging into the modelling. The current work on pre-operative and intra-operative imaging and modelling methods for robotic assisted surgery has to be reviewed and then the Image Constrained Biomechanical Modelling has to be introduced, whereby patient-specific biomechanical modelling is based on the underlying medical images, reducing the computational complexity involved with traditional biomechani-cal modelling. This article concludes with a discussion of the possible and promising future directions of this work.

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An Anisotropic Material Model for Image Guided Neurosurgery

Cited by 6 publications

References 10 publications

Image-Guided Intraoperative Cortical Deformation Recovery Using Game Theory: Application to Neocortical Epilepsy Surgery

Image-Guided Intraoperative Cortical Deformation Recovery Using Game Theory: Application to Neocortical Epilepsy Surgery

Volumetric Intraoperative Brain Deformation Compensation: Model Development and Phantom Validation

In vivo and in situ image guidance and modelling in robotic assisted surgery

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