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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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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

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Published on: July 28, 2013

Precision and accuracy in diffusion tensor magnetic resonance imaging.

Derek K Jones1

  • 1Cardiff University Brain Research Imaging Centre, School of Psychology, and Neuroscience, Cardiff University, Wales, United Kingdom. jonesd27@cf.ac.uk

Topics in Magnetic Resonance Imaging : TMRI
|May 27, 2011
PubMed
Summary
This summary is machine-generated.

Optimizing diffusion magnetic resonance imaging (dMRI) protocols and analysis pipelines significantly enhances data accuracy and precision. Careful choices in imaging and processing reduce the required sample size for reliable clinical research findings.

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Area of Science:

  • Medical Imaging
  • Neuroscience
  • Biophysics

Background:

  • Quantitative metrics from diffusion magnetic resonance imaging (dMRI) are crucial for clinical research.
  • Variability in dMRI data acquisition and processing can impact metric accuracy and precision.

Purpose of the Study:

  • To review key factors influencing accuracy and precision in dMRI quantitative metrics.
  • To guide researchers, especially newcomers, in making informed decisions throughout the dMRI study pipeline.
  • To highlight how methodological choices affect the precision and accuracy of diffusion tensor imaging (DTI) analysis.

Main Methods:

  • Review of the dMRI study pipeline, from imaging protocol selection to data analysis.
  • Focus on preprocessing steps, model fitting, and quantitative estimate extraction.
  • Illustrative examples of how choices impact accuracy and precision.

Main Results:

  • Imaging protocol selection critically affects dMRI data quality.
  • Preprocessing steps and model fitting choices influence the reliability of quantitative metrics.
  • Optimized approaches can substantially reduce the sample size needed for statistical inference.

Conclusions:

  • Informed decisions at each stage of the dMRI pipeline are essential for robust quantitative analysis.
  • Improving accuracy and precision in dMRI is achievable through careful methodological considerations.
  • This review provides practical insights for designing effective dMRI studies and implementing DTI in clinical settings.