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Related Concept Videos

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
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Addressing a systematic vibration artifact in diffusion-weighted MRI.

Daniel Gallichan1, Jan Scholz, Andreas Bartsch

  • 1Centre for Functional Magnetic Resonance Imaging of the Brain, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom. daniel.gallichan@uniklinik-freiburg.de

Human Brain Mapping
|July 16, 2009
PubMed
Summary
This summary is machine-generated.

A patient table vibration artifact in diffusion-weighted MRI causes signal loss, mimicking diffusion changes. This can be mitigated by acquiring full k-space data or using parallel acceleration.

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Area of Science:

  • Medical Imaging
  • Physics

Background:

  • Diffusion-weighted MRI (DWI) is crucial for neuroimaging.
  • Quantitative diffusion measures can be disrupted by imaging artifacts.

Purpose of the Study:

  • To identify and characterize a specific artifact in diffusion-weighted MRI.
  • To investigate the cause and impact of this artifact on diffusion-tensor imaging (DTI).

Main Methods:

  • Analysis of a pronounced artifact observed on a clinical MRI system.
  • Investigating the link between patient table vibrations, gradient switching, and image signal loss.
  • Examining the effect of partial Fourier acquisition and diffusion-gradient directionality.

Main Results:

  • A novel artifact caused by patient table vibrations during diffusion-weighted MRI was identified.
  • The artifact manifests as localized signal loss, particularly with partial Fourier acquisition and left-right diffusion gradients.
  • This signal loss mimics diffusion attenuation, compromising DTI quantitative measures.

Conclusions:

  • The identified artifact significantly impacts DTI data integrity.
  • Strategies to mitigate the artifact include acquiring full k-space data and employing parallel acceleration.
  • A co-regressor approach can improve the interpretation of affected DTI data.