Abstract
MR image preprocessing is a fundamental step to assure the success of any quantitative analysis pipeline. Such preprocessing can be composed of different processes, each of them aimed either to improve image quality or to standardize its geometric and intensity patterns. In this chapter, several of these techniques will be presented and discussed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mohan J, Krishnaveni V, Guo Y. A survey on the magnetic resonance image denoising methods. Biomed Signal Process Control. 2014;9:56–69.
Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Machine Intell. 1990;12:629–39.
Gerig G, Kikinis R, Kubler O, Jolesz FA. Nonlinear anisotropic filtering of MRI data. IEEE Trans Med Imaging. 1992;11:221–32.
Donoho DL, Johnstone IM. Ideal spatial adaptation via wavelet shrinkage. Biometrika. 1994;81:425–55.
Kuwamura S. Wavelet denoising for tomographically reconstructed image. Opt Rev. 2006;13:129–37.
Nowak R. Wavelet-based Rician noise removal for magnetic resonance imaging. IEEE Trans Image Process. 1999;8:1408–19.
Pizurica A, Philips W, Lemahieu I, Acheroy M. A versatile wavelet domain noise filtration technique for medical imaging. IEEE Trans Med Imaging. 2003;22:323–31.
Guleryuz OG. Weighted overcomplete denoising. In: Proceedings of the Asilomar Conference on Signals and Systems. 2003.
Yaroslavsky LP, Egiazarian K, Astola J. Transform domain image restoration methods: review, comparison and interpretation. TICSP Series #9, TUT, Tampere; 2000. ISBN: 952-15-0471-4.
Elad M, Aharon M. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans Image Process. 2006;15(12):3736–45.
Mairal J, Elad M, Sapiro G. Sparse learned representations for image restoration. In: IASC2008. Yokohama; 2008.
Protter M, Elad M. Image sequence denoising via sparse and redundant representations. IEEE Trans Image Process. 2009;18(1):27–36.
Aharon M, Elad M, Bruckstein AM. K-SVD: an algorithm for designing over complete dictionaries for sparse representation. IEEE Trans Sig Process. 2006;54:4311–22.
Bao L, Liu W, Zhu Y, Pu Z, Magnin I. Sparse representation based MRI denoising with total variation. In: ICSP2008 Proceedings. 2008.
Bao L, Robini M, Liu W, Zhu Y. Structure-adaptive sparse denoising for diffusion-tensor MRI. Med Image Anal. 2013;17(4):442–57.
Patel V, Shi, Y, Thompson, PM, Toga, AW. K-SVD for Hardi denoising. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro. 2011.
Buades A, Coll B, Morel JM. A non-local algorithm for image denoising. In: IEEE International Conference on Computer Vision and Pattern Recognition (CPVR), 2005;2: p. 60–5.
Coupé P, Yger P, Prima S, Hellier P, Kervrann C, Barillot C. An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Trans Med Imaging. 2008;27:425–41.
Manjón JV, Carbonell-Caballero J, Lull JJ, Garcia-Martí G, Martí-Bonmatí L, Robles M. MRI denoising using non-local means. Med Image Anal. 2008;4:514–23.
Manjón JV, Tohka J, García-Martí G, et al. Robust MRI brain tissue parameter estimation by multistage outlier rejection. Magn Reson Med. 2008;59(4):866–73.
Manjón JV, Thacker NA, Lull JJ, Garcia-Martí G, Martí-Bonmatí L, Robles M. Multicomponent MR image denoising. Int J Biomed Imaging. Article ID 756897. 2009.
Manjón JV, Coupé P, Martí-Bonmatí L, Robles M, Collins DL. Adaptive non-local means denoising of MR images with spatially varying noise levels. J Magn Reson Imaging. 2010;31:192–203.
Manjón JV, Coupé P, Buades A, Collins DL, Robles M. New methods for MRI denoising based on sparseness and self-similarity. Med Image Anal. 2012;16(1):18–27.
Tristán-Vega A, Aja-Fernández S. DWI filtering using joint information for DTI and HARDI. Med Image Anal. 2010;14(2):205–18.
He L, Greenshields IR. A nonlocal maximum likelihood estimation method for Rician noise reduction in MR images. IEEE Trans Med Imaging. 2009;28:165–72.
Katkovnik V, Foi A, Egiazarian K, Astola J. From local kernel to nonlocal multiple-model image denoising. Int J Comput Vis. 2010;86(1):1–32.
Rajan J, Den Dekker A, Sijbers J. A new non local maximum likelihood estimation method for Rician noise reduction in magnetic resonance images using the Kolmogorov-Smirnov test. Sig Process. 2014;103:16–23.
Manjón JV, Coupé P, Buades A, Fonov V, Louis Collins D, Robles M. Non-local MRI upsampling. Med Image Anal. 2010a;14(6):784–92.
Manjón JV, Coupé P, Buades A, Louis Collins D, Robles M. MRI superresolution using self similarity and image priors. Int J Biomed Imaging. Article ID 425891. 2010b.
J. Rajan, J. Veraat, J.V. Audekerke, M. Verhoye, J. Sijbers, Nonlocal maximumlikelihood estimation method for denoising multiple-coil magnetic resonanceimages, Magn. Reson. Imaging 30 (2012) 1512–1518.
Nicolas Wiest-Daesslé, Sylvain Prima, Pierrick Coupé,Sean Patrick Morrissey, and Christian Barillot. Rician noise removal by non-Local Means filtering for low signal-to-noise ratio MRI: applications to DT-MRI. MICCAI 2008; 11(Pt 2): 171–179.
Manjón JV, Coupe P, Buades A. MRI noise estimation and denoising using non-local PCA. Med Image Anal. 2015;22:35–47.
Manjón JV, Coupé P, Concha L, Buades A, Collins DL, Robles M. Diffusion weighted image denoising using overcomplete local PCA. PLoS One. 2013;8(9), e73021. doi:10.1371/journal.pone.0073021.
Belaroussi B, Milles J, Carme S, Min Zhu Y, Benoit H. Intensity non-uniformity correction in MRI: existing methods and their validation. Med Image Anal. 2005;10:121–32.
Axel L, Constantini J, Listerud J. Intensity correction in surfacecoil MR imaging. Am J Roentgenol. 1987;148:418–20.
Dawant B, Zijdenbos A, Margolin R. Correction of intensity variations in MR images for computer-aided tissue classification. IEEE Trans Med Imaging. 1993;12(4):770–81.
Meyer C, Bland P, Pipe J. Retrospective correction of intensity inhomogeneities in MRI. IEEE Trans Med Imaging. 1995;14(1):36–41.
Wells III W, Grimson W, Kikinis R, Jolesz F. Adaptative segmentation of MRI data. IEEE Trans Med Imaging. 1996;15(4):429–42.
Van Leemput K, Maes F, Vandermeulen D, Suetens P. Automated model-based bias field correction of MR images of the brain. IEEE Trans Med Imaging. 1999;18(10):885–96.
Gispert J, Reig S, Pascau J, Vaquero J, Garca-Barreno P, Desco M. Method for bias field correction of brain T1-weighted magnetic resonance images minimizing segmentation error. Hum Brain Mapp. 2004;22(2):133–44.
Guillemaud R, Brady M. Estimating the bias field of MR images. IEEE Trans Med Imaging. 1997;16(3):238–51.
van Leemput K, Maes F, Vandermeulen D, Suetens P. Automated model-based tissue classification of MR images of the brain. IEEE Trans Med Imag. 1999;18:897–908.
Dempster A, Laird N, Rubing D. Maximum likelihood from incomplete data via EM algorithm. J Royal Soc. 1977;39:1–38.
Ashburner J. Another MRI bias correction approach. In: Eighth International Conference on Functional Mapping of the Human Brain. Sendai; 2002.
Mangin JF. Entropy minimization for automatic correction of intensity nonuniformity. In: IEEE Workshop on Mathematical Methods in Biomedical Image Analysis. 2000. p. 162–9.
Likar B, Viergever M, Pernus F. Retrospective correction of MR intensity inhomogeneity by information minimization. IEEE Trans Med Imaging. 2001;20(12):1398–410.
Vovk U, Pernus F, Likar B. MRI intensity inhomogeneity correction by combining intensity and spatial information. Phys Med Biol. 2004;49(17):4119–33.
Manjón JV, Lull JJ, Carbonell-Caballero J, García-Martí G, Martí-Bonmatí L, Robles M. A nonparametric MRI inhomogeneity correction method. Med Image Anal. 2007;11(4):336–45.
Tustison NJ, Avants BB, Cook P, Zheng Y, Egan A, Yushkevich P, Gee JC. N4ITK: improved N3 bias correction. IEEE Trans Med Imaging. 2010;29(6):1310–20.
Atkins MS, Siu K, Law B, Orchard J, Rosenbaum W. Difficulties of T1 brain MRI segmentation techniques. In: The International Society for Optical Engineering, vol. 4684 of Proceedings of SPIE. 2002. p. 1837–44.
Vansteenkiste E, Vandemeulebroucke J, Philips W. 2D/3D registration of neonatal brain images. In: Proceedings of the The Workshop on Biomedical Image Registration (WBIR ’06), 2006; p. 272–79.
Thevenaz P, Blu T, Unser M. Interpolation revisited. IEEE Trans Med Imaging. 2000;19(7):739–58.
Lehmann TM, G¨onner C, Spitzer K. Survey: interpolation methods in medical image processing. IEEE Trans Med Imaging. 1999;18(11):1049–75.
Carmi E, Liu S, Alon N, Fiat A, Fiat D. Resolution enhancement in MRI. Magn Reson Imaging. 2006;24(2):133–54.
Sijbers J. Signal and noise estimation from magnetic resonance images, Doctoral thesis, Antwepen, 1998.
Kornprobst P, Peelers R, Nikolova M, Deriche R, Ng M, Van Hecke P. A superresolution framework for fMRI sequences and its impact on resulting activation maps. In: Proceedings 6th International Conference Medical Image Computing and Computer-Assisted Intervention (MICCAI ’03). 2003;2879; p. 117–25.
Peled S, Yeshurun Y. Superresolution in MRI: application to human white matter fiber tract visualization by diffusion tensor imaging. Magn Reson Med. 2001;45(1):29–35.
Coupé P, Manjon JV, Chamberland M, Descoteaux M. Collaborative patch-based super-resolution for diffusion-weighted images. Neuroimage. 2013;83:245–61.
Lin S, Liu W, Zhang H, Xie Y, Wang D. A survey of GPU-based medical image computing techniques. Quant Imaging Med Surg. 2012;2(3):188–206.
Klein A, Andersson J, Ardekani BA, Ashburner J, Avants B, et al. Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage. 2009;46:786–802.
Woods RP, Cherry SR, Mazziotta JC. Rapid automated algorithm for aligning and reslicing PET images. J Comput Assist Tomo. 1992;16:620–33.
Collins D, Holmes C, Peters T, Evans A. Automatic 3D model-based neuroanatomical segmentation. Hum Brain Mapp. 1995;3(3):190–208.
Ardekani BA, Bachman AH. Model-based automatic detection of the anterior and posterior commissures on MRI scans. Neuroimage. 2009;46(3):677–82.
Andersson J, Smith S, Jenkinson M. FNIRT—FMRIB’ non-linear image registration tool. Hum Brain Map. 2008; Poster #496.
Rueckert D, Sonoda LI, Hayes C, Hill DLG, Leach MO, Hawkes DJ. Non-rigid registration using free-form deformations: Application to breast MR images. IEEE Trans Med Imaging. 1999;18(8):712–21.
Chiang M-C, Dutton RA, Hayashi KM, Lopez OL, Aizenstein HJ, Toga AW, Becker JT, Thompson PM. 3D pattern of brain atrophy in HIV/AIDS visualized using tensor-based morphometry. Neuroimage. 2007;34:44–60.
Christensen G, Johnson H. Consistent image registration. IEEE Trans Med Imag. 2001;20:568–82.
Ashburner J, Friston KJ. Nonlinear spatial normalization using basis functions. Hum Brain Map. 1999;7:254–66.
Ashburner J. A fast diffeomorphic image registration algorithm. Neuroimage. 2007;38:95–113.
Ashburner J, Friston KJ. Unified segmentation. Neuroimage. 2005;26:839–51.
Gonzalez RC, Woods RE. Digital Image Processing. 3rd ed. Upper Saddle River, NJ: Prentice Hall; 2008. p. 128. ISBN 9780131687288.
Hellier P. Consistent intensity correction of MR images. In: Proceedings of the International Conference on Image Processing (ICIP ’03), 2003; p. 1109–12.
Nyul LG, Udupa JK, Zhang X. New variants of a method of MRI scale standardization. IEEE Trans Med Imaging. 2000;19(2):143–50.
Jager F, Nyul L, Frericks B, Wacker F, Hornegger J. Whole body MRI intensity standardization. In: Horsch A, Deserno TM, Handels H, Meinzer H-P, Tolxdorff T, editors. Bildverarbeitung fur dieMedizin. Berlin: Springer; 2007. p. 459–63.
Lötjönen JM, Wolz R, Koikkalainen JR, et al. Fast and robust multi-atlas segmentation of brain magnetic resonance images. Neuroimage. 2010;49(3):2352–65.
Manjón JV, Eskildsen SF, Coupé P, Romero JE, Louis Collins D, Robles M. Non-local intracranial cavity extraction. IJBI. Article ID 820205. 2014.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Manjón, J.V. (2017). MRI Preprocessing. In: Martí-Bonmatí, L., Alberich-Bayarri, A. (eds) Imaging Biomarkers. Springer, Cham. https://doi.org/10.1007/978-3-319-43504-6_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-43504-6_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-43502-2
Online ISBN: 978-3-319-43504-6
eBook Packages: MedicineMedicine (R0)