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Gapped pulses for frequency-swept MRI.

Djaudat Idiyatullin1, Curt Corum, Steen Moeller

  • 1The Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota, 2021 6th St. SE, Minneapolis, MN 55455, USA. djaudat@cmrr.umn.edu

Journal of Magnetic Resonance (San Diego, Calif. : 1997)
|June 17, 2008
PubMed
Summary
This summary is machine-generated.

A new MRI method, SWIFT (SWeep Imaging with Fourier Transform), uses frequency-swept pulses for fast imaging. This study details implementing hyperbolic secant (HSn) pulses for optimal SWIFT performance and energy efficiency.

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Area of Science:

  • Magnetic Resonance Imaging (MRI)
  • Pulse Sequence Design
  • Biophysics

Background:

  • Standard MRI methods face limitations with samples exhibiting rapid spin-spin relaxation.
  • The SWeep Imaging with Fourier Transform (SWIFT) technique offers a novel approach for such challenging imaging scenarios.
  • SWIFT relies on frequency-swept excitation pulses and time-shared signal acquisition.

Purpose of the Study:

  • To provide experimental guidelines for implementing hyperbolic secant (HSn) pulses within the SWIFT framework.
  • To analyze the behavior of HSn pulses under SWIFT conditions, including rapid passage and time-sharing gaps.
  • To develop methods for estimating key pulse parameters and energy deposition in SWIFT sequences.

Main Methods:

  • Detailed description of experimental procedures for HSn pulse implementation in SWIFT.
  • Investigation of HSn pulse properties in the linear rapid passage regime.
  • Analysis of pulse behavior with added "gaps" for time-shared excitation and acquisition.
  • Derivation of compact expressions for pulse amplitude, flip angle, and energy estimation.

Main Results:

  • HSn pulses are crucial for achieving uniform and broadband spin excitation in SWIFT.
  • The study elucidates the impact of rapid passage and time-sharing gaps on HSn pulse characteristics.
  • Compact formulas are derived for practical estimation of SWIFT pulse parameters and energy deposition.

Conclusions:

  • Proper implementation of HSn pulses is essential for high-quality SWIFT imaging, especially for samples with fast relaxation.
  • The analysis provides a deeper understanding of HSn pulse dynamics within the SWIFT sequence.
  • The derived expressions facilitate efficient and accurate application of SWIFT MRI techniques.