Introduction and Acknowledgements
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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
- 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).
- 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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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 http://www.ncbi.nlm.nih.gov/pubmed/24879923 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).
- 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. http://www.ncbi.nlm.nih.gov/pubmed/24879923
Information for Developers
|Section under construction.|