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Applied Magnetic Resonance

, Volume 44, Issue 3, pp 389–400 | Cite as

Computational Analysis of a Radiofrequency Knee Coil for Low-Field MRI Using FDTD

  • Valentina HartwigEmail author
  • Stefano Tassano
  • Alessio Mattii
  • Nicola Vanello
  • Vincenzo Positano
  • Maria Filomena Santarelli
  • Luigi Landini
  • Giulio Giovannetti
Article

Abstract

Magnetic resonance imaging (MRI) is essential for the diagnosis and treatment of musculoskeletal conditions. Low-field (<0.5T) imaging is a cost-effective alternative to more expensive high-field strength imaging due to the inexpensive setting, greater patient comfort and better safety profile. On the other hand, if compared with high-field body scanners, the low-field scanners produce poor-quality images with lower signal-to-noise ratio. Especially in low-field MR, receiver coil performance plays a significant role in image quality. Coil performance is generally evaluated using classical electromagnetic theory, but when the coil is loaded with a sample, an analytical solution is extremely difficult to derive, so that a trial-and-error approach is often followed. Numerical methods have been proposed in literature as good alternatives to predict MRI coil performance. In this study the performance of a knee coil for low-field (0.5 T) MR scanners is analyzed using workbench tests and numerical simulation with a software program based on the finite difference time domain method. Parameter performances measured using the classical workbench test are compared with those obtained using numerical simulations. Finally, the knee coil performance is validated with images acquired in a commercial low-field MR system.

Keywords

Finite Difference Time Domain Magnetic Field Homogeneity Finite Difference Time Domain Method Coil Sensitivity Knee Coil 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Valentina Hartwig
    • 1
    • 3
    Email author
  • Stefano Tassano
    • 2
  • Alessio Mattii
    • 3
  • Nicola Vanello
    • 3
  • Vincenzo Positano
    • 4
  • Maria Filomena Santarelli
    • 1
  • Luigi Landini
    • 3
    • 4
  • Giulio Giovannetti
    • 1
  1. 1.Institute of Clinical Physiology (IFC)Italian National Research Council (CNR)PisaItaly
  2. 2.Paramed srlGenoaItaly
  3. 3.Department of Information EngineeringUniversity of PisaPisaItaly
  4. 4.Fondazione G. Monasterio CNR-Regione ToscanaPisaItaly

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