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A Quantitative Comparison of the Role of Parameter Selection for Regularization in GRAPPA-Based Autocalibrating Parallel MRI

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1022))

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Abstract

The suitability of regularized reconstruction in autocalibrating parallel magnetic resonance imaging (MRI) is quantitatively analyzed based on the choice of the regularization parameter. In this study, L-curve and generalized cross-validation (GCV) are adopted for parameter selection. The results show that: (1) Presence of well-defined L-corner does not guarantee an artifact-free reconstruction, (2) Sharp L-corners are not always observed in GRAPPA calibration, (3) Parameter values based on L-curves always exceed those based on GCV, and (4) Use of a predetermined number of filters based on the local signal power can result in a compromise between noise and artifacts as well as better visual perception. It is concluded that appropriate use of regularized solutions facilitates minimization of noise build-up in the reconstruction process, without enhancing the effects of aliasing artifacts.

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Acknowledgements

The authors are thankful to Maulana Azad national fellowship by UGC and planning board of Govt. of Kerala (GO(Rt)No. 101/2017/ITD.GOK(02/05/2017)) for the financial assistance.

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Authors and Affiliations

  1. Medical Image Computing and Signal Processing Laboratory, Indian Institute of Information Technology and Management-Kerala (IIITM-K), Trivandrum, Kerala, India

    Raji Susan Mathew & Joseph Suresh Paul

Authors
  1. Raji Susan Mathew
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  2. Joseph Suresh Paul
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Correspondence to Raji Susan Mathew .

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Editors and Affiliations

  1. Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, Kolkata, India

    Bidyut B. Chaudhuri

  2. Department of Advanced Information Technology and Computer Sciences, Tokyo Institute of Agriculture and Technology, Koganei, Tokyo, Japan

    Masaki Nakagawa

  3. Department of Computer Science, Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, Jabalpur, Madhya Pradesh, India

    Pritee Khanna

  4. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Sanjeev Kumar

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Mathew, R.S., Paul, J.S. (2020). A Quantitative Comparison of the Role of Parameter Selection for Regularization in GRAPPA-Based Autocalibrating Parallel MRI. In: Chaudhuri, B., Nakagawa, M., Khanna, P., Kumar, S. (eds) Proceedings of 3rd International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1022. Springer, Singapore. https://doi.org/10.1007/978-981-32-9088-4_4

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  • DOI: https://doi.org/10.1007/978-981-32-9088-4_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-32-9087-7

  • Online ISBN: 978-981-32-9088-4

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