RealTime Simulation and Visualization of Volumetric Brain Deformation from intraoperative MR images

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Real-Time Simulation and Visualization of
Volumetric Brain Deformation from intraoperative
MR images

• M. Ferrant, A. Nabavi, B.Macq,
• R. Kikinis and S.K.Warfield

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Context

• The context of our research is image-guided
  neurosurgery (IGN)
• During IGN, the brain has been observed to deform
  mainly because of gravity, CSF draining/leakage,
  anaesthesiology and tumor resection
• It is therefore of crucial importance to be able
  to correct for these deformations by updating
  pre-operative imaging the surgical planning is
  based upon accordingly.
• Also, it is desirable for the neurosurgeon to
  understand these deformations with appropriate
  visualization tools.

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Previous work and goal

• In our previous work, we have developed a set of
  image processing algorithms for tracking and
  registering intraoperative MR images of the brain
  during neurosurgery using a FE biomechanical
  model.
• We have optimized our algorithms so they can
  execute the registration in a time compatible
  with the constraints of neurosurgery.
• In this paper, our goal was to efficiently
  visualize and characterize the deformation of the
  brain from intraoperative MRI sequences in real
  time.

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Brain Shift During Surgery (1)
Before surgery (1)
Dura opened (2)
Small resection (3)
Tumor resected (4)
Dura closed (5)
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Brain shift during surgery (2)
Time T3 IOP image (resection has started)
Time T4 IOP image (resection progresses)
Time T2 IOP image (after removal of dura)
Time T1 Pre-operative image
Time T5 IOP image (dura closed)
? How to match image at time T1 onto image at
time Ti ?
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Brain shift during surgery (2)
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Overview of deformable registration (1)

• We track landmark surfaces (lateral ventricles,
  cortical surface, tumor if needed) in the image
  sequence and infer the volumetric deformation
  field from the surface deformations using a
  biomechanical FE model.
• Our algorithm consists of 4 main steps
• Generation of a FE tetrahedral model from the
  initial image, with boundary surfaces as landmark
  surfaces,
• Deformation of the boundary surfaces onto
  successive intraoperative images using an active
  surface algorithm,
• Sucessively infer a volumetric deformation field
  using the active surface deformations as boundary
  conditions for the initial FE mesh.
• Update preoperative imaging with the volumetric
  deformation field

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Overview of deformable registration (2)
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Visualization tools

• We implemented a set of visualization modules
  within the VTK library (www.kitware.com) so as to
  be able to efficiently visualize and characterize
  deformation of the FE model throughout the image
  sequence in 2D and 3D.
• Cuts through image data are displayed using
  texture mapping.
• Surface models and volumetric models can be cut
  along arbitrary planes and overlayed on
  corresponding cuts through image data.
• Surface models and volumetric models can be
  color-coded with the intensity of the deformation
  they undergo or other characterization
  parameters.
• Deformation can be visualized as 2D or 3D arrows,
  or as tensor data. (e.g. ellipsoids).

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Pre- and intra-operative Segmentation

• The segmentation serves to extract the key object
  the surgeon wishes to track during surgery.
• Pre-operative segmentation is done accurately
  with time-consuming algorithms
• Intra-operative segmentation is done in real-time
  using the pre-operative segmentation as a
  patient-specific atlas.

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Intra-operative segmentations
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Construction of the FE model

• A multiresolution tetrahedral mesh is extracted
  from the brain volume using an algorithm that we
  specifically designed for generating FE meshes
  from image data. Boundary surfaces can be
  extracted from the volumetric mesh as
  triangulated surfaces.
• The algorithm is available for research purposes

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Visualization of surface deformation (1)

• The algorithm matches the preoperative landmark
  surfaces iteratively onto the successive target
  intraoperative images.

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Visualization of surface deformation (2)
After dural closing
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Visualization of surface deformation (3)
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Topological update of model (1)

• Elements covering the resected areas are removed
  from the mesh and those lying across the brain
  boundary are clipped

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Topological update of model (2)

• Cuts through topologically updated brain surface
  on corresponding intraop scan.

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Computation of Biomechanical Simulation

• Estimate three displacements at each mesh vertex
  of the volumetric FE mesh.
• Active Surface algorithm provides known
  displacements at some mesh vertices (those of the
  boundary surfaces).
• Sparse linear system of equations, depending on
  the connectivity of each vertex in the mesh.
• Resolution of the matrix system equal
  distribution of equations to compute nodes of the
  cluster. Assembly and resolution of matrix system
  are parallelized using MPI and the PETSc library.

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Scaling on parallel cluster

• Timings of biomechanical simulation on Alpha
  cluster with 16 nodes.
• Mesh with about 125000 tetrahedra and 77511
  Equations to solve.

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Visualization of volumetric deformation (1)
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Visualization of volumetric deformation (2)
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Visualization of volumetric deformation (3)
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Visualization of volumetric deformation (4)
3 TO 4
4 TO 5
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Updating preoperative imaging

• The volumetric deformation field can be
  interpolated back onto the image grid to deform
  the intraoperative image at the previous time
  point.

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Landmark deformation analysis

• Landmarks were manually placed on slices of the
  intraop sequence and deformed with the algorithm
  for comparison.
• Mean error was about 2.5 mm

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Characterization of deformation (1)

• We visualize stress-tensors using ellipsoids.
  Color-coding is done with the largest eigenvalue
  of the tensor.

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Characterization of deformation (2)
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Analysis Timeline
Typical times with current implementations on
current hardware.
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Summary

• We have developed and optimized 3D and 2D
  visualization modules for the real-time
  visualization of biomechanical deformation from
  image sequences.
• The visualization tools allow for improved
  characterization, understanding and
  visualization of brain deformation during
  neurosurgery.
• We have optimized our deformable registration
  algorithm by parallelizing it so it runs within
  the time constraints of neurosurgery.

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Future work

• Visualization tools
• plug all visualization modules into a single
  program - ? Into the 3D slicer (D. Gering et al.,
  MICCAI99)
• Algorithmic
• improve surface matching algorithm
• include other structures in the model (e.g.
  falx)
• use more accurate constitutive material equations
  (e.g. visco-elasticity)
• run the algorithm on a large number of cases for
  validation, as well as during surgery.

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Extreme brain shift !? ...