Monte Carlo Method for Uncertainty Propagation in Magnetic Resonance-Based Electric Properties Tomography.

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Title: Monte Carlo Method for Uncertainty Propagation in Magnetic Resonance-Based Electric Properties Tomography.
Authors: Arduino, Alessandro1, Chiampi, Mario1, Pennecchi, Francesca2, Zilberti, Luca2, Bottauscio, Oriano2
Source: IEEE Transactions on Magnetics. Nov2017, Vol. 53 Issue 11, p1-4. 4p.
Subjects: Monte Carlo method, Electric properties, Larmor frequency, Magnetic resonance imaging, Magnetic fields
Abstract: This paper investigates the uncertainty propagation in magnetic resonance-based electric properties tomography, a quantitative imaging technique that recovers the electric properties distribution inside a human body at the Larmor frequency. This is a needed step in order to make the quantitative results reliable for in vivo applications. To this aim, the contrast source inversion method is investigated as a promising technique and the uncertainty propagation through the corresponding model is studied by means of the Monte Carlo method. Thanks to the increased quality in the recovered electric properties, the results suggest that TEM coils for parallel transmission could be a preferable choice. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Magnetics is the property of IEEE 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.)
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  Data: <searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+properties%22">Electric properties</searchLink><br /><searchLink fieldCode="DE" term="%22Larmor+frequency%22">Larmor frequency</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging%22">Magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+fields%22">Magnetic fields</searchLink>
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  Data: This paper investigates the uncertainty propagation in magnetic resonance-based electric properties tomography, a quantitative imaging technique that recovers the electric properties distribution inside a human body at the Larmor frequency. This is a needed step in order to make the quantitative results reliable for in vivo applications. To this aim, the contrast source inversion method is investigated as a promising technique and the uncertainty propagation through the corresponding model is studied by means of the Monte Carlo method. Thanks to the increased quality in the recovered electric properties, the results suggest that TEM coils for parallel transmission could be a preferable choice. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of IEEE Transactions on Magnetics is the property of IEEE 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: 10.1109/TMAG.2017.2713984
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      – Code: eng
        Text: English
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        Type: general
      – SubjectFull: Electric properties
        Type: general
      – SubjectFull: Larmor frequency
        Type: general
      – SubjectFull: Magnetic resonance imaging
        Type: general
      – SubjectFull: Magnetic fields
        Type: general
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              M: 11
              Text: Nov2017
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              Y: 2017
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