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Adaptive Sampling and Reconstruction for Sparse Magnetic Resonance Imaging

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Computational Modeling of Objects Presented in Images

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 15))

Abstract

An adaptive acquisition sequence for Sparse 2D Magnetic Resonance Imaging (MRI) is presented. The method combines random sampling of Cartesian trajectories with an adaptive 2D acquisition of radial projections. It is based on the evaluation of the information content of a small percentage of the k-space data collected randomly to identify radial blades of k-space coefficients having maximum information content. The information content of each direction is evaluated by calculating an entropy function defined on the power spectrum of the projections. The images are obtained by using a non linear reconstruction strategy, based on the homotopic \(\mathrm{L}_{0}\)-norm, on the sparse data. The method is tested on MRI images and it is also compared to the weighted Compressed Sensing. Some results are reported and discussed.

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Acknowledgments

We gratefully acknowledge Abruzzo Region for the financial con-tribution to the project through the European Social Fund (FSE). In addition, we acknowledge Dr. Joshua Trzasko, for having pro-vided useful details to implement the homotopic L0-norm minimiza-tion, and the other members of Computer Vision Laboratory and of AAVI Laboratory for their helpful contribution, in particular Mrs Carmelita Marinelli for technical assistance.

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Authors and Affiliations

  1. Department of Life, Health and Environmental Sciences, University of L’Aquila, Via Vetoio Coppito, 67100, L’Aquila, Italy

    Laura Ciancarella, Danilo Avola & Giuseppe Placidi

Authors
  1. Laura Ciancarella
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  2. Danilo Avola
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  3. Giuseppe Placidi
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Corresponding author

Correspondence to Giuseppe Placidi .

Editor information

Editors and Affiliations

  1. Department of Computer, Control and Management Engineering, Sapienza Università di Roma, Rome, Italy

    Paolo Di Giamberardino

  2. Department of Computer, Control and Management Engineering, Sapienza Università di Roma, Rome, Italy

    Daniela Iacoviello

  3. Department of Mechanical Engineering, Universidade do Porto, Porto, Portugal

    Renato Natal Jorge

  4. Department of mechanical engineering, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

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Ciancarella, L., Avola, D., Placidi, G. (2014). Adaptive Sampling and Reconstruction for Sparse Magnetic Resonance Imaging. In: Di Giamberardino, P., Iacoviello, D., Natal Jorge, R., Tavares, J. (eds) Computational Modeling of Objects Presented in Images. Lecture Notes in Computational Vision and Biomechanics, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-319-04039-4_7

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  • DOI: https://doi.org/10.1007/978-3-319-04039-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-04038-7

  • Online ISBN: 978-3-319-04039-4

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