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Home < Documentation < 4.0 < Modules < N4ITKBiasFieldCorrection

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. The development of this module was supported in part by NIH grants R01 AA016748-01, R01 CA111288 .... check grants.
  • Authors: Nick Tustison (N4ITK filters), Ron Kikinis (PI + design and inspiration), Andrey Fedorov (design, ITK filter modifications, 3D Slicer module)
  • 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.


Links to tutorials that use this module




  • Point to other modules that have similar functionality


  • 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.

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