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Registration of MRI and iUS Data to Compensate Brain Shift Using a Symmetric Block-Matching Based Approach

  • David Drobny
  • Tom Vercauteren
  • Sébastien Ourselin
  • Marc Modat
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11042)

Abstract

This paper describes the application of an established block-matching based registration approach to the CuRIOUS 2018 MICCAI registration challenge. Different variations of this method are compared to demonstrate possible results of a fully automatic and general approach. The results can be used as a reference, for example when evaluating the performance of methods that are specifically developed for ultrasound to MRI registration.

Keywords

Brain shift Fully automatic MRI iUS Symmetric registration Block-matching 

Notes

Acknowledgments

D. Drobny is supported by the UCL EPSRC Centre for Doctoral Training in Medical Imaging and Wellcome/EPSRC Centre for Interventional and Surgical Sciences [NS/A000050/1]. This work is also supported by the Wellcome/EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z] and EPSRC [NS/A000027/1].

References

  1. 1.
    Alexa, M.: Linear combination of transformations. ACM Trans. Graph. 21(3) (2002).  https://doi.org/10.1145/566654.566592
  2. 2.
    Ebner, M., et al.: Volumetric reconstruction from printed films: enabling 30 year longitudinal analysis in MR neuroimaging. NeuroImage 165, 238–250 (2018)CrossRefGoogle Scholar
  3. 3.
    Markiewicz, P.J., et al.: NiftyPET: a high-throughput software platform for high quantitative accuracy and precision PET imaging and analysis. Neuroinformatics 16(1), 95–115 (2017)CrossRefGoogle Scholar
  4. 4.
  5. 5.
    Modat, M., Cash, D.M., Daga, P., Winston, G.P., Duncan, J.S., Ourselin, S.: Global image registration using a symmetric block-matching approach. J. Med. Imaging (Bellingham) 1(2), 024003 (2014)CrossRefGoogle Scholar
  6. 6.
    NiftyReg GitHub page. https://github.com/KCL-BMEIS/niftyreg/wiki. Accessed 29 June 2018
  7. 7.
    Ourselin, S., Roche, A., Subsol, G., Pennec, X., Ayache, N.: Reconstructing a 3D structure from serial histological sections. Image Vis. Comput. 19(1–2), 25–31 (2001).  https://doi.org/10.1016/s0262-8856(00)00052-4CrossRefGoogle Scholar
  8. 8.
    Yiming, X., Maryse, F., Geirmund, U., Hassan, R., Ingerid, R.: REtroSpective Evaluation of Cerebral Tumors (RESECT): a clinical database of pre-operative MRI and intra-operative ultrasound in low-grade glioma surgeries. Med. Phys. 44(7), 3875–3882 (2017)CrossRefGoogle Scholar
  9. 9.
    Yushkevich, P.A., Avants, B.B., Das, S.R., Pluta, J., Altinay, M., Craige, C.: Bias in estimation of hippocampal atrophy using deformation-based morphometry arises from asymmetric global normalization: an illustration in ADNI 3T MRI data. NeuroImage 50(2), 434–445 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Wellcome/EPSRC Centre for Interventional and Surgical SciencesUniversity College LondonLondonUK
  2. 2.School of Biomedical Engineering and Imaging SciencesKing’s College London, King’s Health Partners, St Thomas’ HospitalLondonUK

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