ITK Release 4/A2D2 Projects/Physics-Based Non-rigid Registration: Difference between revisions

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= Goals =
To develop an ITK implementation of physics-based Non-Rigid Registration (NRR) for Image- Guided Surgery (IGS) that will satisfy the following requirements: account for tissue properties in the registration, improve accuracy compared to rigid registration, and reduce execution time to less than one minute.
= Motivation =
= Motivation =
We will develop an ITK implementation of physics-based Non-Rigid Registration (NRR) for Image- Guided Surgery (IGS) that will satisfy the following requirements: account for tissue properties in the registration, improve accuracy compared to rigid registration, and reduce execution time to less than one minute. The benefits to the ITK community are at least two-fold. First, as a stand-alone software we will provide a computationally efficient registration method that accounts for tissue properties and that approximates sparse deformation. Second, through ITK integration into other IGS toolkits, NRR will be part of open-source systems like 3D-Slicer and IGSTK, and commercial systems like BrainLab."
= Goals =


= Deliverable =
The benefits to the ITK community are at least two-fold. First, as a stand-alone software we will provide a computationally efficient registration method that accounts for tissue properties and that approximates sparse deformation. Second, through ITK integration into other IGS toolkits, NRR will be part of open-source systems like 3D-Slicer and IGSTK, and commercial systems like BrainLab.
The deliverable from this project differs from the existing ITK class FEMRegistrationFilter. The methodology is based on the separation of the NRR method into two parts: a regular part, block matching that utilizes the GPU, and an irregular part, a Finite Element solver that is mapped to multi-core processors


# Feature Point Selection filter as 'itk::MaskFeaturePointSelection3D'
# Feature Point Selection filter as 'itk::MaskFeaturePointSelection3D'

Revision as of 09:33, 1 September 2011

Goals

To develop an ITK implementation of physics-based Non-Rigid Registration (NRR) for Image- Guided Surgery (IGS) that will satisfy the following requirements: account for tissue properties in the registration, improve accuracy compared to rigid registration, and reduce execution time to less than one minute.

Motivation

The benefits to the ITK community are at least two-fold. First, as a stand-alone software we will provide a computationally efficient registration method that accounts for tissue properties and that approximates sparse deformation. Second, through ITK integration into other IGS toolkits, NRR will be part of open-source systems like 3D-Slicer and IGSTK, and commercial systems like BrainLab.

  1. Feature Point Selection filter as 'itk::MaskFeaturePointSelection3D'
    1. Input
      1. floating image as itk::Image
      2. Mask image as itk::Image
    2. Output:
      1. list of selected points as itk::PointSet. For each point we store:
        1. image coordinates
        2. real coordinates
        3. tensor
  2. Block Matching filter 'itk::BlockMatching3D'
    1. Input
      1. floating image as itk::Image
      2. fixed image as itk::Image
      3. list of selected points (output of itk::MaskFeaturePointSelection3D) as itk::PointSet
    2. Output:
      1. displacement field as itk::PointSet. For each point we store:
        1. image coordinates
        2. real coordinates
        3. tensor
        4. confidence
        5. displacement
        6. correlation
  3. FEM-based approximation to interpolation filter 'FEMScatteredDataPointSetToImageFilter'


Team

  • Nikos Chrisochoides ( ODU )
  • Andriy Kot ( ODU )
  • Yixun Liu ( ODU )
  • Michel Audette ( ODU )
  • Andinet Enquobahrie ( Kitware )
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