Intensity non-uniformity correction using N3 on 3-T scanners with multichannel phased array coils
- PMID: 18063391
- PMCID: PMC2562663
- DOI: 10.1016/j.neuroimage.2007.10.026
Intensity non-uniformity correction using N3 on 3-T scanners with multichannel phased array coils
Abstract
Measures of structural brain change based on longitudinal MR imaging are increasingly important but can be degraded by intensity non-uniformity. This non-uniformity can be more pronounced at higher field strengths, or when using multichannel receiver coils. We assessed the ability of the non-parametric non-uniform intensity normalization (N3) technique to correct non-uniformity in 72 volumetric brain MR scans from the preparatory phase of the Alzheimer's Disease Neuroimaging Initiative (ADNI). Normal elderly subjects (n=18) were scanned on different 3-T scanners with a multichannel phased array receiver coil at baseline, using magnetization prepared rapid gradient echo (MP-RAGE) and spoiled gradient echo (SPGR) pulse sequences, and again 2 weeks later. When applying N3, we used five brain masks of varying accuracy and four spline smoothing distances (d=50, 100, 150 and 200 mm) to ascertain which combination of parameters optimally reduces the non-uniformity. We used the normalized white matter intensity variance (standard deviation/mean) to ascertain quantitatively the correction for a single scan; we used the variance of the normalized difference image to assess quantitatively the consistency of the correction over time from registered scan pairs. Our results showed statistically significant (p<0.01) improvement in uniformity for individual scans and reduction in the normalized difference image variance when using masks that identified distinct brain tissue classes, and when using smaller spline smoothing distances (e.g., 50-100 mm) for both MP-RAGE and SPGR pulse sequences. These optimized settings may assist future large-scale studies where 3-T scanners and phased array receiver coils are used, such as ADNI, so that intensity non-uniformity does not influence the power of MR imaging to detect disease progression and the factors that influence it.
Figures








Similar articles
-
Improvement of brain segmentation accuracy by optimizing non-uniformity correction using N3.Neuroimage. 2009 Oct 15;48(1):73-83. doi: 10.1016/j.neuroimage.2009.06.039. Epub 2009 Jun 25. Neuroimage. 2009. PMID: 19559796
-
Longitudinal stability of MRI for mapping brain change using tensor-based morphometry.Neuroimage. 2006 Jun;31(2):627-40. doi: 10.1016/j.neuroimage.2005.12.013. Epub 2006 Feb 15. Neuroimage. 2006. PMID: 16480900 Free PMC article.
-
Whole brain quantitative T2 MRI across multiple scanners with dual echo FSE: applications to AD, MCI, and normal aging.Neuroimage. 2010 Aug 15;52(2):508-14. doi: 10.1016/j.neuroimage.2010.04.255. Epub 2010 May 2. Neuroimage. 2010. PMID: 20441797 Free PMC article.
-
Synthetic T1-weighted brain image generation with incorporated coil intensity correction using DESPOT1.Magn Reson Imaging. 2006 Nov;24(9):1241-8. doi: 10.1016/j.mri.2006.03.015. Epub 2006 Sep 18. Magn Reson Imaging. 2006. PMID: 17071345
-
Uniform combined reconstruction of multichannel 7T knee MRI receive coil data without the use of a reference scan.J Magn Reson Imaging. 2019 Nov;50(5):1534-1544. doi: 10.1002/jmri.26691. Epub 2019 Feb 19. J Magn Reson Imaging. 2019. PMID: 30779475
Cited by
-
A subspace-based coil combination method for phased-array magnetic resonance imaging.Magn Reson Med. 2016 Feb;75(2):762-74. doi: 10.1002/mrm.25664. Epub 2015 Mar 13. Magn Reson Med. 2016. PMID: 25772460 Free PMC article.
-
Errors in Quantitative Image Analysis due to Platform-Dependent Image Scaling.Transl Oncol. 2014 Feb 1;7(1):65-71. doi: 10.1593/tlo.13811. eCollection 2014 Feb. Transl Oncol. 2014. PMID: 24772209 Free PMC article.
-
Trait positive affect is associated with hippocampal volume and change in caudate volume across adolescence.Cogn Affect Behav Neurosci. 2015 Mar;15(1):80-94. doi: 10.3758/s13415-014-0319-2. Cogn Affect Behav Neurosci. 2015. PMID: 25231241
-
Visceral fat is associated with brain structure independent of human immunodeficiency virus infection status.J Neurovirol. 2017 Jun;23(3):385-393. doi: 10.1007/s13365-016-0507-7. Epub 2016 Dec 15. J Neurovirol. 2017. PMID: 27981440 Free PMC article.
-
Intensity Inhomogeneity Correction of Structural MR Images: A Data-Driven Approach to Define Input Algorithm Parameters.Front Neuroinform. 2016 Mar 15;10:10. doi: 10.3389/fninf.2016.00010. eCollection 2016. Front Neuroinform. 2016. PMID: 27014050 Free PMC article.
References
-
- Arnold JB, Liow JS, Schaper KA, Stern JJ, Sled JG, Shattuck DW, Worth AJ, Cohen MS, Leahy RM, Mazziotta JC, Rottenberg DA. Qualitative and quantitative evaluation of six algorithms for correcting intensity nonuniformity effects. NeuroImage. 2001;13:931–943. - PubMed
-
- Ashburner J, Csernansky JG, Davatzikos C, Fox NC, Frisoni GB, Thompson PM. Computer-assisted imaging to assess brain structure in healthy and diseased brains. Lancet Neurol. 2003;2:79–88. - PubMed
-
- Belaroussi B, Milles J, Carme S, Zhu YM, Benoit-Cattin H. Intensity non-uniformity correction in MRI: existing methods and their validation. Med. Image Anal. 2006;10:234–246. - PubMed
-
- Bernstein MA, Huston J, Ward HA. Imaging artifacts at 3.OT. J. Magn. Reson. Imaging. 2006;24:735–746. - PubMed
-
- Collins CM, Liu WZ, Schreiber W, Yang QX, Smith MB. Central brightening due to constructive interference with, without, and despite dielectric resonance. J. Magn. Reson. Imaging. 2005;21:192–196. - PubMed
Publication types
MeSH terms
Grants and funding
LinkOut - more resources
Full Text Sources
Other Literature Sources
Medical