Abstract
The reconstruction in MRI assumes a uniform radio-frequency field. However, this is violated, which leads to anatomically inconsequential intensity non-uniformities. An anatomic region can be imaged with multiple contrasts that result in different non-uniformities. A method is presented for the joint intensity uniformity restoration of two such images. The effect of the intensity distortion on the auto-co-occurrence statistics of each image as well as on the joint-co-occurrence statistics of the two images is modeled. Their non-stationary deconvolution gives Bayesian coring estimates of the images. Further constraints for smoothness, stability, and validity of the non-uniformity estimates are also imposed. The effectiveness and accuracy of the method has been demonstrated extensively with both BrainWeb phantom images as well as with real brain anatomic data of 29 Parkinson’s disease patients.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cocosco, C., Kollokian, V., Kwan, R.S., Evans, A.: BrainWeb: online interface to a 3D MRI simulated brain database. NeuroImage 5(4–2/4), S425 (1997)
Fan, A., Wells III, W.M., Fisher III, J.W., Çetin, M., Haker, S., Mulkern, R., Tempany, C., Willsky, A.S.: A unified variational approach to denoising and bias correction in MR. In: Taylor, C., Noble, J.A. (eds.) IPMI 2003. LNCS, vol. 2732, pp. 148–159. Springer, Heidelberg (2003). doi:10.1007/978-3-540-45087-0_13
Hadjidemetriou, S., Buechert, M., Ludwig, U., Hennig, J.: Joint restoration of bi-contrast MRI data for spatial intensity non-uniformities. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 346–358. Springer, Heidelberg (2011). doi:10.1007/978-3-642-22092-0_29
Hadjidemetriou, S., Studholme, C., Mueller, S., Weiner, M., Schuff, N.: Restoration of MRI data for intensity non-uniformities using local high order intensity statistics. Med. Image Anal. 13(1), 36–48 (2009)
Li, C., Gore, J., Davatzikos, C.: Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation. Magn. Reson. Imaging 32, 913–923 (2014)
Li, C., Huang, R., Ding, Z., Gatenby, J., Metaxas, D., Gore, J.: A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans. Image Process. 20(7), 2007–2016 (2011)
Lui, D., Modhafar, A., Haider, M., Wong, A.: Monte Carlo-based noise compensation in coil intensity corrected endorectal MRI. BMC Med. Imaging 15, 43 (2015)
Mangin, J.: Entropy minimization for automatic correction of intensity nonuniformity. In: Proceedings of IEEE Workshop on MMBIA, pp. 162–169 (2000)
Learned-Miller, E.G., Jain, V.: Many heads are better than one: jointly removing bias from multiple MRIs using nonparametric maximum likelihood. In: Christensen, G.E., Sonka, M. (eds.) IPMI 2005. LNCS, vol. 3565, pp. 615–626. Springer, Heidelberg (2005). doi:10.1007/11505730_51
Noterdaeme, O., Brady, M.: A fast method for computing and correcting intensity inhomogeneities in MRI. In: Proceedings of ISBI, pp. 1525–1528 (2008)
Renugadevi, M., Varghese, D., Vaithiyanathan, V., Raju, N.: Variational level set segmentation and bias correction of fused medical images. Asian J. Med. Sci. 4(2), 66–74 (2012)
Simoncelli, E., Adelson, E.: Noise removal via Bayesian wavelet coring. In: Proceedings of 3rd IEEE ICIP, vol. I, pp. 379–382 (1996)
Sled, J., Zijdenbos, A., Evans, A.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imaging 17(1), 87–97 (1998)
Smith, S.: Fast robust automated brain extraction. Proc. Hum. Brain Mapp. 17, 143–155 (2002)
Vidal-Pantaleoni, A., Mart, D.: Comparison of different speckle reduction techniques in SAR images using wavelet transform. Int. J. Remote Sens. 25(22), 4915–4932 (2004)
Vovk, U., Pernus, F., Likar, B.: Intensity inhomogeneity correction of multispectral MR images. NeuroImage 32, 54–61 (2006)
Zhang, H., Ye, X., Chen, Y.: An efficient algorithm for multiphase image segmentation with intensity bias correction. IEEE Trans. Image Process. 22(10), 3842–3851 (2013)
Zheng, Y., Gee, J.: Estimation of image bias field with sparsity constraints. In: Proceedings of ISBI, pp. 255–262 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Hadjidemetriou, S., Psychogios, M.N., Lingor, P., von Eckardstein, K., Papageorgiou, I. (2017). Restoration of Intensity Uniformity of Bi-contrast MRI Data with Bayesian Co-occurrence Coring. In: Valdés Hernández, M., González-Castro, V. (eds) Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-60964-5_54
Download citation
DOI: https://doi.org/10.1007/978-3-319-60964-5_54
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60963-8
Online ISBN: 978-3-319-60964-5
eBook Packages: Computer ScienceComputer Science (R0)