On the Effects of Model Complexity in Computing Brain Deformation for Image-Guided Neurosurgery

Ma, Junjie; Wittek, Adam; Zwick, Benjamin F.; Joldes, Grand Roman; Warfield, Simon K.; Miller, Karol

doi:10.1007/978-1-4419-9619-0_6

Cited by 4 publications

(5 citation statements)

References 16 publications

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“…The difficulties associated with segmentation of tumors when building finite element meshes for biomechanical models of the brain were discussed in our previous study. 48 It is worth noting, however, that the analysis of sensitivity of computed brain deformations to the complexity of the biomechanical models used 27 demonstrates that even assigning exactly the same material properties to the tumor as to the rest of the parenchyma, therefore avoiding tumor segmentation entirely, leads to only minimal (and for practical purposes negligible) deterioration in the accuracy of predicted displacements.…”

Section: Discussionmentioning

confidence: 99%

Biomechanical Model as a Registration Tool for Image-Guided Neurosurgery: Evaluation Against BSpline Registration

et al. 2013

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In this paper we evaluate the accuracy of warping of neuro-images using brain deformation predicted by means of a patient-specific biomechanical model against registration using a BSpline-based free form deformation algorithm. Unlike the Bspline algorithm, biomechanics-based registration does not require an intra-operative MR image which is very expensive and cumbersome to acquire. Only sparse intra-operative data on the brain surface is sufficient to compute deformation for the whole brain. In this contribution the deformation fields obtained from both methods are qualitatively compared and overlaps of Canny edges extracted from the images are examined. We define an edge based Hausdorff distance metric to quantitatively evaluate the accuracy of registration for these two algorithms. The qualitative and quantitative evaluations indicate that our biomechanics-based registration algorithm, despite using much less input data, has at least as high registration accuracy as that of the BSpline algorithm.

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Section: Discussionmentioning

confidence: 99%

Biomechanical Model as a Registration Tool for Image-Guided Neurosurgery: Evaluation Against BSpline Registration

et al. 2013

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“…The maximum displacement applied was 21.9 mm. The brain deformation problem with displacement loading is a Dirichlet-type problem and therefore the computed displacements are only weakly sensitive to the assumed material model (Wittek et al, 2009;Ma et al, 2011;Miller and Lu, 2013).…”

Section: Patient-specific Geometry Brain Deformation Due To Intracran...mentioning

confidence: 99%

Patient-specific solution of the electrocorticography forward problem in deforming brain

Zwick¹,

Bourantas²,

Safdar³

et al. 2021

Preprint

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Invasive intracranial electroencephalography (iEEG) or electrocorticography (ECoG) measures electrical potential directly on the surface of the brain, and, combined with numerical modeling, can be used to inform treatment planning for epilepsy surgery. Accurate solution of the iEEG or ECoG forward problem, which is a crucial prerequisite for solving the inverse problem in epilepsy seizure onset localization, requires accurate representation of the patient's brain geometry and tissue electrical conductivity after implantation of electrodes. However, implantation of subdural grid electrodes causes the brain to deform, which invalidates preoperatively acquired image data. Moreover, postoperative magnetic resonance imaging (MRI) is incompatible with implanted electrodes and computed tomography (CT) has insufficient range of soft tissue contrast, which precludes both MRI and CT from being used to obtain the deformed postoperative geometry. In this paper, we present a biomechanics-based image warping procedure using preoperative MRI for tissue classification and postoperative CT for locating implanted electrodes to perform non-rigid registration of the preoperative image data to the postoperative configuration. We solve the iEEG forward problem on the predicted postoperative geometry using the finite element method (FEM) which accounts for patient-specific inhomogeneity and anisotropy of tissue conductivity. Results for the simulation of a current source in the brain show large differences in electrical potential (RDM ≥ 0.18 and MAG ≥ 0.84) predicted by the models based on the original images and the deformed images corresponding to the brain geometry deformed by placement of invasive electrodes. Computation of the leadfield matrix (useful for solution of the iEEG inverse problem) also showed significant differences between the different models. The results suggest that significant improvements in source localization accuracy may be realized by the application of the proposed modeling methodology.

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“…Image-based methods yield an anatomical model that provides a static geometrical representation that needs to be complemented with a biomechanical model 94,95 adding a level of complexity. Non-linear finite element strategies can then be used to implement patient-specific models that are used to predict brain shift deformation.…”

Section: Image Acquisitionmentioning

confidence: 99%

“…Non-linear finite element strategies can then be used to implement patient-specific models that are used to predict brain shift deformation. [94][95][96] Devising many of these patient-specific brain deformation models can have high computation times and can therefore be costly. 94 Speed needs to be weighed with robustness and accuracy in creating a suitable brain-shift deformation model that is personalized, effective, and yet sufficiently generalizable.…”

Section: Image Acquisitionmentioning

confidence: 99%

A Methodology for Systematic Volumetric Analysis of Perioperative Cranial Imaging in Neurosurgical Patients

Atsina¹,

Gorniak²,

Sharan³

et al. 2016

JHN Journal

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On the Effects of Model Complexity in Computing Brain Deformation for Image-Guided Neurosurgery

Cited by 4 publications

References 16 publications

Biomechanical Model as a Registration Tool for Image-Guided Neurosurgery: Evaluation Against BSpline Registration

Biomechanical Model as a Registration Tool for Image-Guided Neurosurgery: Evaluation Against BSpline Registration

Patient-specific solution of the electrocorticography forward problem in deforming brain

A Methodology for Systematic Volumetric Analysis of Perioperative Cranial Imaging in Neurosurgical Patients

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