The fast automatic algorithm for correction of MR bias field

Journal of Magnetic Resonance Imaging

Volumn 24, Issue 4, 2006, Pages 891-900

  Milchenko, Mikhail V ^a,c   Pianykh, Oleg S ^b   Tyler, John M ^a  

^a Louisiana State University   (United States)

^b LOUISIANA STATE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c NONE   (United States)

Author keywords

Bias correction; Magnetic resonance; MR artifact; MR nonuniformity; Surface fitting

Indexed keywords

ALGORITHM; ARTICLE; ARTIFACT REDUCTION; BIAS FIELD; BRAIN; COMPUTER SIMULATION; DERIVATIVE SURFACE FITTING; DIAGNOSTIC ERROR; IMAGE ANALYSIS; IMAGE PROCESSING; IMAGE QUALITY; INTERNAL BIAS; MEASUREMENT; NUCLEAR MAGNETIC RESONANCE IMAGING; PHANTOM; PRIORITY JOURNAL; SENSITIVITY AND SPECIFICITY; SIMULATION; THORAX;

ALGORITHMS; ARTIFACTS; BRAIN MAPPING; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; PHANTOMS, IMAGING; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; THORAX;

EID: 33749600720     PISSN: 10531807     EISSN: 15222586     Source Type: Journal    
DOI: 10.1002/jmri.20695     Document Type: Article

References (25)

1. Condon BR, Patterson J, Wyper D, Jenkins A, Hadley DM. Image non-uniformity in magnetic resonance imaging: its magnitude and methods for its correction. Br J Radiol 1987;60:83-87.
2. Wells WM III, Grimson WEL, Kikinis R, Jolesz FA. Adaptive segmentation of MRI data. IEEE Trans Med Imaging 1996;15:429-442.
3. Ahmed MN, Samen M, Yamany SM, Farag AA, Moriarty T. A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans Med Imaging 2002;21:193-199.
4. Lai SH, Fang M. A dual image approach for bias field correction in magnetic resonance imaging. Magn Reson Imaging 2003;21:121-125.
5. Studholme C, Cardenas V, Song E, Ezekiel F, Maudsley A, Weiner M. Accurate template-based correction of brain MRI intensity distortion with application to dementia and aging. IEEE Trans Med Imaging 2004;23:99-110.
6. Guillemaud R, Brady M. Estimating the bias field of MR images. IEEE Trans Med Imaging 1997;16:238-251.
7. Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 2001;20:45-57.
8. Sled JG. Zijdenbos AP. Evans AC. A nonparametric method for automatic correction of intensity inhomogeneity in MRI data. IEEE Trans Med Imaging 1998;7:87-97.
9. Dempster AP, Laird NM, Rubin DB. Maximum likelihood from in-complete data via the EM algorithm J R Stat Soc 1977;39:1-38.
10. Brinkmann BH, Manduca A, Robb RA. Optimized homomorphic uns harp masking for MR grayscale inhomogeneity correction. IEEE Trans Med Imaging 1998;17:161-171.
11. Christensen JD. Normalization of brain magnetic resonance images using histogram even-order derivative analysis. Magn Reson Imaging 2003;21:817-20.
12. DeCarli C. Murphy DG, Teichberg D, Campbell G, Sobering GS. Local histogram correction of MRI spatially dependent image pixel intensity nonuniformity. J Magn Reson Imaging 1996;6:519-528.
13. Vokurka EA, Thacker NA, Jackson A. A fast model independent method for automatic correction of intensity nonuniformity in MRI data. J Magn Reson Imaging 1999;10:550-562.
14. Lai SH, Ming Fang M. A new variational shape-form-orientation approach to correcting intensity inhomogeneitles in magnetic resonance images. Medical Image Analysis 1999;4:409-424.
15. Likar B, Viergever MA, Permis F. Retrospective correction of MR intensity inhomogeneity by information minimization. IEEE Trans Med Imaging 2001;20:1398-1410.
16. Brechbühler C, Gerig G, Czakely G. Compensation of spatial inhomogeneity in MRI based on a parametric bias estimate. In: Proceedings of the Fourth International Conference on Visualization in Biomedical Computing. London: Springer-Verlag: 1996. p. 141-146.
17. Styner M, Brechbuehler C. Székely G, Gerig G. Parametric estimate of intensity inhomogeneities applied to MRI. IEEE Trans Med Imaging 2000;19:153-165.
18. Liew AW, Yan H. An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation. IEEE Trans Med Imaging 2003;22:1063-1075.
19. Lee SK, Vannier MW. Post-acquisition correction of MR inhomogeneities. Magn Reson Med 1996;36:275-286.
20. Zhou LQ, Zhu YM, Bergot C. et al. A method of radio-frequency inhomogeneity correction for brain tissue segmentation in MRI. Comput Med Imaging Graph 2001;25:379-389.
21. SPM: Statistical Parametric Mapping software. Available at: http://www.fil.ion.ucl.ac.uk/spm. Last accessed: 14 July 2006.
22. Ashburner J, Friston K. MRI sensitivity correction and tissue classification. Neuroimage 1998;7:S706.
23. Arnold JB, Liow JS, Schaper KA, et al. Qualitative and quantitative evaluation of six algorithms for correcting intensity nonuniformity effects. Neuroimage 2001;13:931-943.
24. BrainWeb: Simulated Brain Database. Available at: http://www. bic.mni.mcgill.ca/brainweb. Last accessed: 14 July 2006.
25. Center for Morphometric Analysis, Massachusetts General Hospital. Internet Brain Segmentation Repository (IBSR). Manually-segmented MR volume archive. Available at: http://www.cma.mgh. harvard.edu/ibsr. Last accessed: 14 July 2006.