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Related Concept Videos

Brain Imaging01:14

Brain Imaging

385
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
385

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Related Experiment Video

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
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Same Brain, Different Look?-The Impact of Scanner, Sequence and Preprocessing on Diffusion Imaging Outcome

Ronja Thieleking1, Rui Zhang1, Maria Paerisch1

  • 1Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany.

Journal of Clinical Medicine
|November 13, 2021
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Summary

MRI scanner hardware and software significantly impact diffusion-weighted imaging (DWI) outcomes like fractional anisotropy (FA) and mean diffusivity (MD). These variations can mask or mimic pathological changes, emphasizing the need for data harmonization in neuroimaging research.

Keywords:
ageingdiffusion magnetic resonance imagingfractional anisotropyimaging artefactsmulti-centrereproducibilitywhite matter

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Area of Science:

  • Neuroimaging
  • Diffusion-weighted imaging (DWI)
  • Quantitative MRI

Background:

  • Reproducibility of MRI assessments is critical for clinical diagnostics and longitudinal studies.
  • Advances in MRI hardware and software can introduce artefactual changes or mask pathological alterations.
  • The impact of MRI hardware, protocol, and software changes on neuroimaging outcomes, particularly DWI, is understudied.

Purpose of the Study:

  • To compare DWI outcomes and artefact severity across different MRI scanners and sequences.
  • To investigate the influence of imaging site and preprocessing software on diffusion tensor imaging (DTI) metrics.
  • To assess the variability of fractional anisotropy (FA) and mean diffusivity (MD) values.

Main Methods:

  • 121 healthy participants (age 19-54) underwent two matched DWI protocols on Siemens 3T Magnetom Verio and Skyrafit scanners.
  • Tensor fitting was used to obtain fractional anisotropy (FA) and mean diffusivity (MD) maps.
  • Tract-based spatial statistics (TBSS) was applied to skeletonised FA and MD values.

Main Results:

  • Inter-scanner and inter-sequence variability in skeletonised FA reached up to 5%, varying in magnitude and direction across brain regions.
  • Skeletonised MD values showed differences up to 14% between scanners.
  • DTI outcome measures demonstrated strong dependence on imaging site and software, with regionally inhomogeneous biases.

Conclusions:

  • Diffusion tensor imaging (DTI) outcome measures are significantly influenced by imaging site and software, introducing biases that vary by brain region.
  • These biases can exceed and confound physiological effects like ageing.
  • There is a critical need to harmonize neuroimaging data acquisition and analysis to enhance reliability and replicability.