Difference between revisions of "2014 Summer Project Week:An ITK implementation of Physics-Based Non-Rigid Registration method for Brain Shift"

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Image:PBNRRSlicer.jpg | The PBNRR CLI module. Top Left: moving image, top right: moving image volume render, bottom left: fixed image, bottom right: warped moving image.  
 
Image:PBNRRSlicer.jpg | The PBNRR CLI module. Top Left: moving image, top right: moving image volume render, bottom left: fixed image, bottom right: warped moving image.  
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Image:PBNRR_UI.jpg | PBNRR's UI.
 
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Revision as of 01:14, 23 June 2014

Home < 2014 Summer Project Week:An ITK implementation of Physics-Based Non-Rigid Registration method for Brain Shift

Key Investigators

  • Fotis Drakopoulos (CRTC)
  • Yixun Liu (CRTC)
  • Andriy Kot (CRTC)
  • Andrey Fedorov (BWH/SPL)
  • Olivier Clatz (Asclepios,INRIA)
  • Ron Kikinis (BWH/SPL)
  • Nikos Chrisochoides (CRTC)

Project Description

This project creates a CLI Slicer module for the ITK implementation of a Physics-Based Non-Rigid Registration (PBNRR) method. The PBNRR compensates for the brain shift during the Image-Guided Neurosurgery (IGNS). The method uses a linear homogeneous bio-mechanical model to compute a dense deformation field that defines a transformation for every point in the fixed image to the moving image. The PBNRR includes the following three components combine together to provide a user-friendly interface.

  • Feature Point Selection.

This component selects highly discriminant features (blocks of voxels) from the moving image. We use the variance of the image intensity within the block region to measure its relevance and only select a fraction of all potential blocks based on a predefined parameter of the algorithm.

  • Block Matching.

The block matching algorithm searches the fixed image for the corresponding position (that maximizes a similarity measure) of the selected feature blocks voxels in the moving image.

  • Finite Element Solver.

This component builds a Finite Element (FE) bio-mechanical model, applies the block matching displacements to the model, and estimates the mesh deformations iteratively by first using an approximation method. Once many of the outliers from block matching are rejected, we use an interpolation formulation to compute the image deformation field that maps positions from the fixed to the moving coordinate frame.

Objective

  • Develop a CLI Slicer module for the PBNRR framework.

Approach, Plan

Progress

  • The implemented CLI module is currently tested on various MRI data.

References

  • An ITK implementation of a physics-based non-rigid registration method for brain deformation in image-guided neurosurgery.

Liu Y, Kot A, Drakopoulos F, Yao C, Fedorov A, Enquobahrie A, Clatz O and Chrisochoides NP (2014), Front. Neuroinform. 8:33. doi: 10.3389/fninf.2014.00033

  • Non-Rigid Registration for Brain MRI: Faster and Cheaper.

Yixun Liu, Andrey Fedorov, Ron Kikinis and Nikos Chrisochoides. Published in International Journal of Functional Informatics and Personalized Medicine (IJFIPM), Publisher Inderscience Enterprises Ltd., Volume 3, No. 1, pages 48 -- 57, 2010