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Introduction and Acknowledgements

This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the NA-MIC website. Author: Daniel Blezek Contributor: Jim Miller, GE Contact: Daniel Blezek

GE   Surgical Planning Laboratory

Module Description

Registers two images together using an affine transform and mutual information. This module is often used to align images of different subjects or images of the same subject from different modalities.

This module can smooth images prior to registration to mitigate noise and improve convergence. Many of the registration parameters require a working knowledge of the algorithm although the default parameters are sufficient for many registration tasks.

Use Cases

Most frequently used for these scenarios:

• Use Case 1:

Affine Registration Defaults

Before Registration

After Registration

• Use Case 2:

In this example, the Affine Registration module is used to register the mean fMRI image to the baseline DWI image of the same subject. The fMRI data is motion corrected and slice timing. The first four volumes are discarded for stability reasons. The data is then averaged over time to compute the mean fMRI image. The diffusion weighted images are eddy-current corrected and smoothed. The tensors and baseline image are extracted using the Diffusion Tensor Estimation module. These images are loaded into Slicer, and the Register-Affine Registration module is selected:

Affine Registration Defaults

Before Registration

After Registration

The fixed image is set to the DWI baseline and the moving image is set to the mean fMRI. The DWI baseline resolution is 1.67x1.67x1.7mm and the fMRI resolution is 1.875x1.875x3mm. The default settings are sufficient to register these images.

Tutorials

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Panels and their use

Parameters:

• Preprocessing
  • Smoothing level for fixed image: Amount of smoothing applied to fixed image prior to registration. Default is 0 (none). Range is 0-5 (unitless). Consider smoothing the input data if there is considerable amounts of noise or the noise pattern in the fixed and moving images is very different.
  • Smoothing level for moving image: Amount of smoothing applied to moving image prior to registration. Default is 0 (none). Range is 0-5 (unitless). Consider smoothing the input data if there is considerable amounts of noise or the noise pattern in the fixed and moving images is very different.
• Registration Parameters
  • Histogram Bins: Number of histogram bins to use for Mattes Mutual Information. Reduce the number of bins if a registration fails. If the number of bins is too large, the estimated PDFs will be a field of impulses and will inhibit reliable registration estimation.
  • Spatial Samples: Number of spatial samples to use in estimating Mattes Mutual Information. Larger values yield more accurate PDFs and improved registration quality.
  • Iterations: Number of iterations
  • Translation scaling: Relative scale of translations to rotations, i.e. a value of 100 means 10mm = 1 degree. (Actual scale used is 1/(TranslationScale^2)). This parameter is used to "weight" or "standardized" the transform parameters and their effect on the registration objective function.
• IO
  • Initial transform: Initial transform for aligning the fixed and moving image. Maps positions in the fixed coordinate frame to positions in the moving coordinate frame. Optional.
  • Fixed Image: Fixed image to which to register
  • Moving Image: Moving image
  • Output transform: Transform calculated that aligns the fixed and moving image. Maps positions in the fixed coordinate frame to the moving coordinate frame. Optional (specify an output transform or an output volume or both).
  • Output Volume: Resampled moving image to the fixed image coordinate frame. Optional (specify an output transform or an output volume or both).

Similar Modules

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References

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Information for Developers

Section under construction.