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

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Project Description

This project implements a Physics-Based Non-Rigid Registration (PBNRR) framework to compensate for the brain shift during an Image-Guided Neurosurgery (IGNS). The method uses a linear homogeneous biomechanical 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.

It 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 of the selected voxels in the moving image, that maximizes a similarity measure.

  • Finite Element Solver.

This component builds a Finite Element (FE) biomechanical 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, Andriy 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