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Low-field magnetic resonance imaging using multiplicative regularization.

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Title: Low-field magnetic resonance imaging using multiplicative regularization.
Authors: de Leeuw den Bouter, Merel1 (AUTHOR) M.L.deLeeuwdenBouter-1@tudelft.nl, van Gijzen, Martin1 (AUTHOR), Remis, Rob2 (AUTHOR)
Source: Magnetic Resonance Imaging (0730725X). Jan2021, Vol. 75, p21-33. 13p.
Subjects: Image reconstruction algorithms, Regularization parameter, Image denoising, Algorithms, Scanning systems
Abstract: In this paper we present a magnetic resonance imaging (MRI) technique that is based on multiplicative regularization. Instead of adding a regularizing objective function to a data fidelity term, we multiply by such a regularizing function. By following this approach, no regularization parameter needs to be determined for each new data set that is acquired. Reconstructions are obtained by iteratively updating the images using short-term conjugate gradient-type update formulas and Polak-Ribière update directions. We show that the algorithm can be used as an image reconstruction algorithm and as a denoising algorithm. We illustrate the performance of the algorithm on two-dimensional simulated low-field MR data that is corrupted by noise and on three-dimensional measured data obtained from a low-field MR scanner. Our reconstruction results show that the algorithm effectively suppresses noise and produces accurate reconstructions even for low-field MR signals with a low signal-to-noise ratio. Graphical abstract Unlabelled Image • We employ a magnetic resonance imaging technique particularly suited for low-field scanners. • Regularization is included by multiplying a data misfit functional by a total variation objective function. • No extra computations are required to determine an effective regularization parameter. • The technique successfully reconstructs images from noise-corrupted simulated data and measured low-field MRI data [ABSTRACT FROM AUTHOR]
Copyright of Magnetic Resonance Imaging (0730725X) is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 147113450
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  Data: <searchLink fieldCode="JN" term="%22Magnetic+Resonance+Imaging+%280730725X%29%22">Magnetic Resonance Imaging (0730725X)</searchLink>. Jan2021, Vol. 75, p21-33. 13p.
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  Data: In this paper we present a magnetic resonance imaging (MRI) technique that is based on multiplicative regularization. Instead of adding a regularizing objective function to a data fidelity term, we multiply by such a regularizing function. By following this approach, no regularization parameter needs to be determined for each new data set that is acquired. Reconstructions are obtained by iteratively updating the images using short-term conjugate gradient-type update formulas and Polak-Ribière update directions. We show that the algorithm can be used as an image reconstruction algorithm and as a denoising algorithm. We illustrate the performance of the algorithm on two-dimensional simulated low-field MR data that is corrupted by noise and on three-dimensional measured data obtained from a low-field MR scanner. Our reconstruction results show that the algorithm effectively suppresses noise and produces accurate reconstructions even for low-field MR signals with a low signal-to-noise ratio. Graphical abstract Unlabelled Image • We employ a magnetic resonance imaging technique particularly suited for low-field scanners. • Regularization is included by multiplying a data misfit functional by a total variation objective function. • No extra computations are required to determine an effective regularization parameter. • The technique successfully reconstructs images from noise-corrupted simulated data and measured low-field MRI data [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Magnetic Resonance Imaging (0730725X) is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.mri.2020.10.001
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      – Code: eng
        Text: English
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        PageCount: 13
        StartPage: 21
    Subjects:
      – SubjectFull: Image reconstruction algorithms
        Type: general
      – SubjectFull: Regularization parameter
        Type: general
      – SubjectFull: Image denoising
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Scanning systems
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      – TitleFull: Low-field magnetic resonance imaging using multiplicative regularization.
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            NameFull: de Leeuw den Bouter, Merel
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            NameFull: van Gijzen, Martin
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            NameFull: Remis, Rob
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            – D: 01
              M: 01
              Text: Jan2021
              Type: published
              Y: 2021
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              Value: 75
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