Abstract
Although N3 is perhaps the most widely used method for MRI bias field correction, its underlying mechanism is in fact not well understood. Specifically, the method relies on a relatively heuristic recipe of alternating iterative steps that does not optimize any particular objective function. In this paper we explain the successful bias field correction properties of N3 by showing that it implicitly uses the same generative models and computational strategies as expectation maximization (EM) based bias field correction methods. We demonstrate experimentally that purely EM-based methods are capable of producing bias field correction results comparable to those of N3 in less computation time.
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References
Wells, W.M., Grimson, W.E.L., Kinikis, R., Jolesz, F.A.: Adaptive segmentation of MRI data. IEEE Transactions on Medical Imaging 15(4), 429–442 (1996)
Held, K., Kops, E., Krause, B., Wells, W., Kikinis, R., Muller-Gartner, H.: Markov random field segmentation of brain MR images. IEEE Transactions on Medical Imaging 16(6), 878–886 (1997)
Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: Automated model-based bias field correction of MR images of the brain. IEEE Transactions on Medical Imaging 18(10), 885 (1999)
Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: Automated model-based tissue classification of MR images of the brain. IEEE Transactions on Medical Imaging 18(10), 897–908 (1999)
Pham, D., Prince, J.: Adaptive fuzzy segmentation of magnetic resonance images. IEEE Transactions on Medical Imaging 18(9), 737–752 (1999)
Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging 20(1), 45–57 (2001)
Ashburner, J., Friston, K.J.: Unified segmentation. NeuroImage 26(3), 839–851 (2005)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological) 39(1), 1–38 (1977)
Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Transactions on Medical Imaging 17(1), 87–97 (1998)
Minka, T.P.: Expectation-maximization as lower bound maximization (1998)
Boyes, R.G., Gunter, J.L., Frost, C., Janke, A.L., Yeatman, T., Hill, D.L., Bernstein, M.A., Thompson, P.M., Weiner, M.W., Schuff, N., Alexander, G.E., Killiany, R.J., DeCarli, C., Jack, C.R., Fox, N.C.: Intensity non-uniformity correction using N3 on 3-T scanners with multichannel phased array coils. NeuroImage 39(4), 1752–1762 (2008)
Likar, B., Viergever, M.A., Pernus, F.: Retrospective correction of MR intensity inhomogeneity by information minimization. IEEE Transactions on Medical Imaging 20(12), 1398–1410 (2001)
Zheng, W., Chee, M.W., Zagorodnov, V.: Improvement of brain segmentation accuracy by optimizing non-uniformity correction using N3. NeuroImage 48(1), 73–83 (2009)
Tustison, N., Avants, B., Cook, P., Zheng, Y., Egan, A., Yushkevich, P., Gee, J.: N4ITK: Improved N3 bias correction. IEEE Transactions on Medical Imaging 29(6), 1310–1320 (2010)
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Larsen, C.T., Iglesias, J.E., Van Leemput, K. (2014). N3 Bias Field Correction Explained as a Bayesian Modeling Method. In: Cardoso, M.J., Simpson, I., Arbel, T., Precup, D., Ribbens, A. (eds) Bayesian and grAphical Models for Biomedical Imaging. Lecture Notes in Computer Science, vol 8677. Springer, Cham. https://doi.org/10.1007/978-3-319-12289-2_1
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DOI: https://doi.org/10.1007/978-3-319-12289-2_1
Publisher Name: Springer, Cham
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