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Registration of Dynamic Contrast Enhanced MRI with Local Rigidity Constraint

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7359))

Abstract

Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) of the kidney provides important information for the diagnosis of renal dysfunction. To this end, a time series of image volumes is acquired after injection of a contrast agent. The interpretation and pharmacokinetic analysis of the time series data is highly sensitive to motion artifacts. Registration of these data is a challenging task as contrast uptake adds new image features and gives rise to intensity changes over time within the kidneys.

This paper presents a new registration pipeline for a time series of 3D DCE-MRI. The pipeline combines state-of-art modules such as a weighted and robust least squares type distance measure, a regularization that is based on hyperelasticity and thus ensures diffeomorphic transformations and enables the incorporation of local rigidity constraints on the kidneys. We provide results that indicate the necessity of these constraints and illustrate the superiority of the proposed pipeline as compared to other approaches.

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References

  1. Barrett, R.: Templates for the solution of linear systems. building blocks for iterative methods. Society for Industrial Mathematics (1994)

    Google Scholar 

  2. Burger, M., Modersitzki, J., Ruthotto, L.: A hyperelastic regularization energy for image registration. SIAM Journal on Scientific Computing (in revision) (2012)

    Google Scholar 

  3. Greif, C., Schötzau, D.: Preconditioners for saddle point linear systems with highly singular (1, 1) blocks. Electronic Transactions on Numerical Analysis 22, 114–121 (2006)

    MathSciNet  MATH  Google Scholar 

  4. Haber, E., Modersitzki, J.: Intensity gradient based registration and fusion of multi-modal images. Methods of Information in Medicine 46(3), 292–299 (2007)

    Google Scholar 

  5. Haber, E., Heldmann, S., Modersitzki, J.: A framework for image-based constrained registration with an application to local rigidity. Linear Algebra and its Applications 431, 459–470 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  6. Hodneland, E., Kjorstad, A., Andersen, E., Monssen, J.A., Lundervold, A., Rørvik, J., Munthe-Kaas, A.: In vivo estimation of glomerular filtration in the kidney using DCE-MRI. In: Image and Signal Processing and Analysis (ISPA), pp. 755–761. IEEE (2011)

    Google Scholar 

  7. Melbourne, A., Atkinson, D., White, M.J., Collins, D., Leach, M., Hawkes, D.: Registration of dynamic contrast-enhanced MRI using a progressive principal component registration (PPCR). Physics in Medicine and Biology 52(17), 5147–5156 (2007)

    Article  Google Scholar 

  8. Michoux, N., Vallee, J., Pechere-Bertschi, A., Montet, X., Buehler, L., Van Beers, B.: Analysis of contrast-enhanced MR images to assess renal function. Magnetic Resonance Materials in Physics, Biology and Medicine 19(4), 167–179 (2006)

    Article  Google Scholar 

  9. Modersitzki, J.: FAIR: Flexible algorithms for image registration (2009)

    Google Scholar 

  10. Nocedal, J., Wright, S.J.: Numerical optimization. Springer (1999)

    Google Scholar 

  11. Rogelj, P., Zöllner, F.G., Kovačič, S., Lundervold, A.: Motion correction of contrast-enhanced MRI time series of kidney. In: Proceedings of the 16th International Electrotechnical and Computer Science Conference (ERK 2007), pp. 191–194 (2007)

    Google Scholar 

  12. Staring, M., Klein, S., Pluim, J.: A rigidity penalty term for nonrigid registration. Medical Physics 34, 4098 (2007)

    Article  Google Scholar 

  13. Viola, P., Wells III, W.M.: Alignment by maximization of mutual information. International Journal of Computer Vision 24(2), 137–154 (1997)

    Article  Google Scholar 

  14. Zöllner, F.G., Sance, R., Rogelj, P., Ledesma-Carbayo, M.J., Rørvik, J., Santos, A., Lundervold, A.: Assessment of 3D DCE-MRI of the kidneys using non-rigid image registration and segmentation of voxel time courses. Computerized Medical Imaging and Graphics 33(3), 171–181 (2009)

    Article  Google Scholar 

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© 2012 Springer-Verlag Berlin Heidelberg

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Ruthotto, L., Hodneland, E., Modersitzki, J. (2012). Registration of Dynamic Contrast Enhanced MRI with Local Rigidity Constraint. In: Dawant, B.M., Christensen, G.E., Fitzpatrick, J.M., Rueckert, D. (eds) Biomedical Image Registration. WBIR 2012. Lecture Notes in Computer Science, vol 7359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31340-0_20

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  • DOI: https://doi.org/10.1007/978-3-642-31340-0_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31339-4

  • Online ISBN: 978-3-642-31340-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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