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An Approximation Approach to Measurement Design in the Reconstruction of Functional MRI Sequences

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Brain and Health Informatics (BHI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8211))

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Abstract

The reconstruction quality of a functional MRI sequence is not only determined by the reconstruction algorithms but also by the information obtained from measurements. This paper addresses the measurement design problem of selecting k feasible measurements such that the mutual information between the unknown image and measurements is maximized, where k is a given budget. To calculate the mutual information, we utilize correlations of adjacent functional MR images via modelling an fMRI sequence as a linear dynamical system with an identity transition matrix. Our model is based on the key observation that variations of functional MR images are sparse over time in the wavelet domain. In cases where this sparsity constraint obtains, the measurement design problem is intractable. We therefore propose an approximation approach to resolve this issue. The experimental results demonstrate that the proposed approach successes in reconstructing functional MR images with greater accuracy than by random sampling.

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Author information

Authors and Affiliations

  1. Imperial College London, London, UK

    Shulin Yan, Lei Nie, Chao Wu & Yike Guo

  2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Lei Nie

Authors
  1. Shulin Yan
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  2. Lei Nie
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  3. Chao Wu
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  4. Yike Guo
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Editor information

Editors and Affiliations

  1. Department of Systems Life Engineering, Maebashi Institute of Technology, 460-1 Kamisadori-cho, 371-0816, Maebashi, Gunma, Japan

    Kazayuki Imamura

  2. Electronics-Inspired Interdisciplinary Research Institute, Toyohashi University of Technology, 1-1 Hibarigaoka Tenpaku-cho, 441-8580, Toyohashi, Aichi, Japan

    Shiro Usui

  3. Department of Neurobiology and Behavior, Gunma University Graduate School of Medicine, 39-22 Showamachi 3-chome, 371-8511, Maebashi, Gunma, Japan

    Tomoaki Shirao

  4. The Smith Kettlewell Eye Research Institute, 2318 Fillmore Street, 94115, San Francisco, CA, USA

    Takuji Kasamatsu

  5. University of Rostock, 18051, Rostock, Germany

    Lars Schwabe

  6. Department of Life Science and Informatics, Maebashi Institute of Technology, 460-1 Kamisadori-cho, 371-0816, Maebashi, Gunma, Japan

    Ning Zhong

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© 2013 Springer International Publishing Switzerland

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Yan, S., Nie, L., Wu, C., Guo, Y. (2013). An Approximation Approach to Measurement Design in the Reconstruction of Functional MRI Sequences. In: Imamura, K., Usui, S., Shirao, T., Kasamatsu, T., Schwabe, L., Zhong, N. (eds) Brain and Health Informatics. BHI 2013. Lecture Notes in Computer Science(), vol 8211. Springer, Cham. https://doi.org/10.1007/978-3-319-02753-1_12

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  • DOI: https://doi.org/10.1007/978-3-319-02753-1_12

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02752-4

  • Online ISBN: 978-3-319-02753-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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