Abstract
The reconstruction of MRI data assumes a uniform radio-frequency field. However, in practice the radio-frequency field is inhomogeneous and leads to non-biological intensity non-uniformities across an image. This artifact can complicate further automated analysis of the data. In general, an acquisition protocol provides images of the same anatomic region with multiple contrasts representing similar underlying information, but suffering from different intensity 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 of the two images as well as on their joint-co-occurrence statistics is modeled and used for their restoration with Wiener filtering. Several regularity constrains for the anatomy and for the non-uniformity are also imposed. Moreover, the method considers an inevitable difference between the signal regions of the two images. The joint treatment of the images can improve the accuracy and the efficiency of the restoration as well as decrease the requirements for additional calibration scans. The effectiveness of the method has been demonstrated extensively with both phantom and real brain anatomic data as well as with real DIXON pairs of fat and water abdominal data.
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
Preview
Unable to display preview. Download preview PDF.
References
Arvis, V., Debain, C., Berducat, M., Benassi, A.: Generalization of the cooccurrence matrix for colour images: Application to colour texture classification. Image Anal. Stereol. 23, 63–72 (2004)
Belaroussi, B., Milles, J., Carme, S., Zhu, Y., Cattin, H.: Intensity non-uniformity correction in MRI: Existing methods and their validation. Medical Image Analysis 10, 234–246 (2006)
Brainard, D., Wandell, B.: Analysis of the retinex theory of color vision. Journal of the Optical Society of America A 3(10), 1651–1661 (1986)
Brinkmann, B., Manduca, A., Robb, R.: Optimized homomorphic unsharp masking for MR grayscale intensity correction. IEEE Trans. on Medical Imaging 17(2), 161–171 (1998)
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, W.M., Fisher, J.W., Çetin, M., Haker, S., Mulkern, R.V., Tempany, C., Willsky, A.S.: A unified variational approach to denoising and bias correction in MR. In: Taylor, C.J., Noble, J.A. (eds.) IPMI 2003. LNCS, vol. 2732, pp. 148–159. Springer, Heidelberg (2003)
Gudbjartsson, H., Patz, S.: The Rician distribution of noisy MRI data. Magnetic Resonance in Medicine 34, 910–914 (1995)
Hadjidemetriou, S., Studholme, C., Mueller, S., Weiner, M., Schuff, N.: Restoration of MRI data for intensity non-uniformities using local high order intensity statistics. Medical Image Analysis 13(1), 36–48 (2009)
Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: Automated model based bias field correction of MR images of the brain. IEEE Trans. on Medical Imaging 18(10), 885–896 (1999)
Luo, J., Zhu, Y., Clarysse, P., Magnin, I.: Correction of bias field in MR images using singularity function analysis. IEEE Trans. on Medical Imaging 24(8), 1067–1085 (2005)
Mangin, J.: Entropy minimization for automatic correction of intensity nonuniformity. In: Proc. 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)
Noterdaeme, O., Brady, M.: A fast method for computing and correcting intensity inhomogeneities in MRI. In: Proc. of ISBI, pp. 1525–1528 (2008)
Sled, J., Zijdenbos, A., Evans, A.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. on Medical Imaging 17(1), 87–97 (1998)
Smith, S.: Fast robust automated brain extraction. Proc. of Human Brain Mapping 17, 143–155 (2002)
Studholme, C.: RView, http://www.colin-studholme.net/
Vovk, U., Pernus, F., Likar, B.: Intensity inhomogeneity correction of multispectral MR images. NeuroImage 32, 54–61 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hadjidemetriou, S., Buechert, M., Ludwig, U., Hennig, J. (2011). Joint Restoration of Bi-contrast MRI Data for Spatial Intensity Non-uniformities. In: Székely, G., Hahn, H.K. (eds) Information Processing in Medical Imaging. IPMI 2011. Lecture Notes in Computer Science, vol 6801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22092-0_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-22092-0_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22091-3
Online ISBN: 978-3-642-22092-0
eBook Packages: Computer ScienceComputer Science (R0)