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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 1–9Cite as

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Accelerated Diffusion Spectrum Imaging with Compressed Sensing Using Adaptive Dictionaries

Accelerated Diffusion Spectrum Imaging with Compressed Sensing Using Adaptive Dictionaries

  • Berkin Bilgic19,
  • Kawin Setsompop20,21,
  • Julien Cohen-Adad20,21,
  • Van Wedeen20,21,
  • Lawrence L. Wald20,21,22 &
  • …
  • Elfar Adalsteinsson19,22 
  • Conference paper
  • 4226 Accesses

  • 5 Citations

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7512)

Abstract

Diffusion Spectrum Imaging (DSI) offers detailed information on complex distributions of intravoxel fiber orientations at the expense of extremely long imaging times (~1 hour). It is possible to accelerate DSI by sub-Nyquist sampling of the q-space followed by nonlinear reconstruction to estimate the diffusion probability density functions (pdfs). Recent work by Menzel et al. imposed sparsity constraints on the pdfs under wavelet and Total Variation (TV) transforms. As the performance of Compressed Sensing (CS) reconstruction depends strongly on the level of sparsity in the selected transform space, a dictionary specifically tailored for sparse representation of diffusion pdfs can yield higher fidelity results. To our knowledge, this work is the first application of adaptive dictionaries in DSI, whereby we reduce the scan time of whole brain DSI acquisition from 50 to 17 min while retaining high image quality. In vivo experiments were conducted with the novel 3T Connectome MRI, whose strong gradients are particularly suited for DSI. The RMSE from the proposed reconstruction is up to 2 times lower than that of Menzel et al.’s method, and is actually comparable to that of the fully-sampled 50 minute scan. Further, we demonstrate that a dictionary trained using pdfs from a single slice of a particular subject generalizes well to other slices from the same subject, as well as to slices from another subject.

Keywords

  • Sparse Representation
  • Compress Sensing
  • Reconstruction Error
  • Compress Sensing Reconstruction
  • Diffusion Spectrum Image

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.

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Author information

Authors and Affiliations

  1. Massachusetts Institute of Technology, MA, USA

    Berkin Bilgic & Elfar Adalsteinsson

  2. A.A. Martinos Center for Biomedical Imaging, MA, USA

    Kawin Setsompop, Julien Cohen-Adad, Van Wedeen & Lawrence L. Wald

  3. Harvard Medical School, MA, USA

    Kawin Setsompop, Julien Cohen-Adad, Van Wedeen & Lawrence L. Wald

  4. Harvard-MIT Division of Health Sciences and Technology, MA, USA

    Lawrence L. Wald & Elfar Adalsteinsson

Authors
  1. Berkin Bilgic
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  2. Kawin Setsompop
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  3. Julien Cohen-Adad
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  4. Van Wedeen
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  5. Lawrence L. Wald
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  6. Elfar Adalsteinsson
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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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Bilgic, B., Setsompop, K., Cohen-Adad, J., Wedeen, V., Wald, L.L., Adalsteinsson, E. (2012). Accelerated Diffusion Spectrum Imaging with Compressed Sensing Using Adaptive Dictionaries. 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 7512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33454-2_1

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  • DOI: https://doi.org/10.1007/978-3-642-33454-2_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33453-5

  • Online ISBN: 978-3-642-33454-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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