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Introduction and Acknowledgements


Information on NA-MIC can be obtained from the NA-MIC website.
Contact: Andrey Fedorov, <email></email>


Module Description

Use Cases

This module is especially appropriate for these use cases:

  • Image acquisition is done with a surface coil, and you observe signal fall-off as you get further away from the coil (this is particularly the case at higher field strengths)
  • You observe smooth variation of the intensity over the tissue that should have intensity close to uniform
  • Your attempts to segment or register your data are not successful, and you are not sure what to do next

Examples of the module in use:

  • Correction of the signal inhomogeneity in prostate MRI obtained with endorectal coil at 3T field strength
Prostate erMRI
After bias correction
Before - After subtract image
Recovered bias field
  • Correction of the bias in vervet MRI. Acquisition parameters: 3T GE scanner, single-channel dedicated RF coil (Litzcage, Doty Scientific, Columbia, SC); 3D SPGR sequence (TI 600ms, TE 3.276ms, TR 15.28ms; flip angle 15 deg; matrix 256x256; FOV 12cm; in-plane resolution 0.47 mm; slice thickness 0.5 mm).
Slice of the input test volume with apparent bias field and label outlines
Output image visually contains less inhomogeneity
Before - After subtract image
Recovered bias field
  • Bias correction in brain MRI: It was shown that the result of bias correction in brain MRI is significantly improved when the bias estimation is limited to the brain region (see Boyes et al. in References). You might be able to achieve better results for your application if you provide a meaningful mask that corresponds to the structure of interest, or to a structure that should be homogeneous, as a parameter to this module.





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       ** ': 
        *** ': 

List of parameters generated transforming this XML file using this XSL file. To update the URL of the XML file, edit this page.


Selection of the mask used by the algorithm is a crucial consideration in using the N4 algorithm. Use of the mask is always recommended, if possible. The following is the communication from the author of the algorithm, Nick Tustison, on the advanced use of the mask:

A more sophisticated use of the mask option is described here

specifically the subsection tiled “Atropos six-tissue segmentation” (page 171)
and it concerns the situation where one is performing segmentation and 
using the segmentation results to improve your bias field and vice versa.  We 
use a “pure tissue” weight mask at each iteration (bias field estimation <—> 
segmentation) as one of the inputs to N4 to get improved results.  This pure 
tissue weight mask is described by Equation (1) on page 172.  The basic
idea is that you want to avoid using the pixels that have poor classification
in your bias field estimation (i.e. pixels on the gray/white matter interface).

Similar Modules



  • Tustison N, Gee J N4ITK: Nick's N3 ITK Implementation For MRI Bias Field Correction, The Insight Journal 2009 January-June link
  • Tustison N, Avants B, Cook P, Gee J N4ITK: Improved N3 Bias Correction with Robust B-Spline Approximation, Proc. of ISBI'10, 2010
  • Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC N4ITK: Improved N3 Bias Correction, IEEE Trans Med Imag, 2010 link
  • Boyes RG, Gunter JL, Frost C, Janke AL, Yeatman T, Hill DL, Bernstein MA, Thompson PM, Weiner MW, Schuff N, Alexander GE, Killiany RJ, DeCarli C, Jack CR, Fox NC (2008) Intensity non-uniformity correction using N3 on 3-T scanners with multichannel phased array coils. NeuroImage 39:1752-62 link.
  • Tustison, Nicholas J., Philip A. Cook, Arno Klein, Gang Song, Sandhitsu R. Das, Jeffrey T. Duda, Benjamin M. Kandel et al. "Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements." Neuroimage 99 (2014): 166-179.

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