Intensity inhomogeneity correction of multispectral MR images

NeuroImage
Uros VovkBostjan Likar

Abstract

Intensity inhomogeneity in MR images is an undesired phenomenon, which often hampers different steps of quantitative image analysis such as segmentation or registration. In this paper, we propose a novel fully automated method for retrospective correction of intensity inhomogeneity. The basic assumption is that inhomogeneity correction could be improved by integrating spatial and intensity information from multiple MR channels, i.e., T1, T2, and PD weighted images. Intensity inhomogeneities of such multispectral images are removed simultaneously in a four-step iterative procedure. First, the probability distribution of image intensities and corresponding spatial features is calculated. In the second step, intensity correction forces that tend to minimize joint entropy of multispectral image are estimated for all image voxels. Third, independent inhomogeneity correction fields are obtained for each channel by regularization and normalization of voxel forces, and last, corresponding partial inhomogeneity corrections are performed separately for each channel. The method was quantitatively evaluated on simulated and real MR brain images and compared to three other methods.

References

Mar 1, 1988·Medical Physics·W W Brey, P A Narayana
Jan 1, 1987·The British Journal of Radiology·B R CondonD M Hadley
Sep 1, 1995·Journal of Magnetic Resonance Imaging : JMRI·R P VelthuizenM L Silbiger
Jun 9, 1998·IEEE Transactions on Medical Imaging·J G SledA C Evans
Nov 26, 1999·IEEE Transactions on Medical Imaging·D L Pham, J L Prince
Jan 11, 2000·IEEE Transactions on Medical Imaging·K Van LeemputP Suetens
Jun 30, 2000·IEEE Transactions on Medical Imaging·M StynerG Gerig
Jan 29, 2002·IEEE Transactions on Medical Imaging·B LikarF Pernus
May 7, 2002·IEEE Transactions on Medical Imaging·Mohamed N AhmedThomas Moriarty
Jan 22, 2003·Journal of Neuroscience Methods·Anders H AndersenDon M Gash
Apr 3, 2003·Magnetic Resonance Imaging·Shang-Hong Lai, Ming Fang
Sep 6, 2003·IEEE Transactions on Medical Imaging·Alan Wee-Chung Liew, Hong Yan
Dec 9, 2003·Journal of Neuroscience Methods·Umberto AmatoBruno Alfano
Aug 25, 2004·NeuroImage·Emma B Lewis, Nicholas C Fox
Oct 9, 2004·Physics in Medicine and Biology·Uros VovkBostjan Likar
Jan 1, 1996·IEEE Transactions on Medical Imaging·B JohnstonM Anderson
Jan 1, 1996·IEEE Transactions on Medical Imaging·W M WellsF A Jolesz
Jan 1, 1993·IEEE Transactions on Medical Imaging·B M DawantR A Margolin

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Citations

Oct 2, 2013·Proceedings of the National Academy of Sciences of the United States of America·Ajay B SatputeLisa Feldman Barrett
Oct 25, 2006·Cerebral Cortex·Mark Schram ChristensenJens Bo Nielsen
Jul 23, 2011·Medical Physics·Jeremy D P Hoisak, David A Jaffray
May 21, 2013·Brain : a Journal of Neurology·Stefano SandroneGianvito Martino
Sep 1, 2012·AJNR. American Journal of Neuroradiology·M H LeeJ S Shimony
Mar 16, 2007·IEEE Transactions on Medical Imaging·Uros VovkBostjan Likar
Apr 6, 2016·Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences·Catie ChangJeff H Duyn
Apr 18, 2015·Brain : a Journal of Neurology·Damian M HerzHartwig R Siebner
Feb 28, 2017·PloS One·Robin StrandJoel Kullberg
Jan 9, 2016·Journal of Applied Physiology·Miikka-Juhani HonkaPirjo Nuutila
Mar 25, 2020·Journal of Medical Imaging·Eva BreznikRobin Strand

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