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Related Experiment Videos

Reducing motion artifacts in two-dimensional Fourier transform imaging.

E M Haacke1, J L Patrick

  • 1Case Western Reserve University, Department of Radiology, Cleveland, OH 44106.

Magnetic Resonance Imaging
|January 1, 1986
PubMed
Summary
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Motion in two-dimensional Fourier transform imaging (2DFT) causes image ghosting artifacts. New methods like ROPE and COPE reorder data acquisition to effectively reduce these artifacts without increasing scan time.

Area of Science:

  • Medical Imaging
  • Image Processing
  • Physics

Background:

  • Motion artifacts are a significant challenge in two-dimensional Fourier transform imaging (2DFT).
  • These artifacts, often appearing as image replication or "ghosting," degrade diagnostic quality.
  • Understanding the physics of motion's impact on 2DFT is crucial for artifact mitigation.

Purpose of the Study:

  • To analyze the effects of periodic motion on 2DFT imaging.
  • To derive expressions for the position and amplitude of motion-induced ghosting artifacts.
  • To propose and evaluate methods for reducing these artifacts.

Main Methods:

  • Mathematical modeling and simulation of periodic motion in 2DFT.
  • Derivation of ghost position and amplitude based on motion parameters.

Related Experiment Videos

  • Evaluation of artifact reduction techniques including randomization, averaging, respiratory gating, and novel methods like ROPE and COPE.

    • Main Results:

    • Periodic motion in 2DFT imaging predictably causes image ghosting.
    • Simulations demonstrated image degradation due to pulsatile flow and in-plane motion.
    • Methods like ROPE and COPE effectively reduce ghosting by reordering data acquisition.

    Conclusions:

    • Motion artifacts, specifically ghosting, are inherent to 2DFT imaging but can be mitigated.
    • ROPE (Respiratory Ordered Phase Encoding) and COPE (Correlated Prospective Evaluation) offer efficient solutions by altering data acquisition timing.
    • Advanced techniques involving motion modeling and generalized transform inversion hold potential for recovering ideal image resolution.