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International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2012: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 pp 138–145Cite as

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Fast Diffusion Tensor Registration with Exact Reorientation and Regularization

Fast Diffusion Tensor Registration with Exact Reorientation and Regularization

  • Junning Li19,
  • Yonggang Shi19,
  • Giang Tran21,
  • Ivo Dinov19,
  • Danny J. J. Wang20 &
  • …
  • Arthur W. Toga19 
  • Conference paper
  • 3964 Accesses

  • 1 Citations

  • 1 Altmetric

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7511)

Abstract

Diffusion tensor imaging is widely used in brain connectivity study. As more and more group studies recruit a large number of subjects, it is important to design registration methods that are not only theoretically rigorous, but also computationally efficient, for processing large data sets. However, the requirement of reorienting diffusion tensors complicates and slows down the registration, especially for those methods whose scalar-image versions have linear complexity, for example, the Demons algorithm. In this paper, we propose an extension of the Demons algorithm that incorporates exact reorientation and regularization into the calculation of deforming velocity, yet preserving its linear complexity. This method restores the computational efficiency of the Demons algorithm to diffusion images, but does not sacrifice registration goodness. In our experiments, the new algorithm achieved state-of-art performance at a ten-fold decrease of computational time.

Keywords

  • Fractional Anisotropy
  • Energy Function
  • Image Registration
  • Linear Complexity
  • Descent Direction

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This work is supported by grants K01EB013633, R01MH080892 9P41EB015922, P41 RR013642, R01MH71940, U54RR021813, U24RR025736 from NIH.

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Author information

Authors and Affiliations

  1. Laboratory of Neuro Imaging, University of California, Los Angeles, CA, USA

    Junning Li, Yonggang Shi, Ivo Dinov & Arthur W. Toga

  2. Brain Mapping Center, Department of Neurology, University of California, Los Angeles, CA, USA

    Danny J. J. Wang

  3. Department of Mathematics, University of California, Los Angeles, CA, USA

    Giang Tran

Authors
  1. Junning Li
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  2. Yonggang Shi
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  3. Giang Tran
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  4. Ivo Dinov
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  5. Danny J. J. Wang
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  6. Arthur W. Toga
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Editor information

Editors and Affiliations

  1. Project Team Asclepios, Inria Sophia Antipolis, 06902, Sophia-Antipolis, France

    Nicholas Ayache & Hervé Delingette & 

  2. MIT, CSAIL, 02139, Cambridge, MA, USA

    Polina Golland

  3. Information and Communication Headquarters, Nagoya University, 464-8603, Nagoya, Japan

    Kensaku Mori

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© 2012 Springer-Verlag Berlin Heidelberg

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Li, J., Shi, Y., Tran, G., Dinov, I., Wang, D.J.J., Toga, A.W. (2012). Fast Diffusion Tensor Registration with Exact Reorientation and Regularization. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7511. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33418-4_18

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  • DOI: https://doi.org/10.1007/978-3-642-33418-4_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33417-7

  • Online ISBN: 978-3-642-33418-4

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