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Related Experiment Video

Updated: Mar 15, 2026

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
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Basic MR sequence parameters systematically bias automated brain volume estimation.

Sven Haller1,2, Pavel Falkovskiy3,4, Reto Meuli4

  • 1Faculty of Medicine, University of Geneva, Geneva, Switzerland. sven.haller@me.com.

Neuroradiology
|September 15, 2016
PubMed
Summary
This summary is machine-generated.

Modifying brain MRI scan parameters like resolution or contrast can significantly bias automated morphometry results. These changes, up to 5%, can mimic early Alzheimer disease volume alterations, necessitating standardized protocols.

Keywords:
3D T1HippocampusMRIVolumetry

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Area of Science:

  • Neuroimaging
  • Medical Physics
  • Radiology

Background:

  • Automated brain MRI morphometry, including hippocampal volumetry, is crucial for Alzheimer disease diagnosis.
  • A growing number of software tools are available for brain MRI analysis.
  • Clinical routine MRI protocol parameters may systematically bias automated segmentation results.

Purpose of the Study:

  • To investigate the impact of simple MR protocol parameter modifications on automated brain MRI segmentation.
  • To determine if changes in spatial resolution, contrast, or filtering bias morphometry results.

Main Methods:

  • 20 patients undergoing 1.5T brain MRI for cognitive decline workup were included.
  • Three 3D T1 MPRAGE sequences with varying parameters (resolution, filtering) were compared.
  • Automated brain segmentation was performed using FreeSurfer and MorphoBox software.

Main Results:

  • Spatial resolution and contrast modifications led to volume differences up to 4.28% in cortical gray matter and 4.16% in the hippocampus.
  • Image data filtering resulted in volume differences up to 5.48% in cortical gray matter.
  • These volume differences were statistically significant (p < 0.001 to p < 0.05).

Conclusions:

  • Simple changes in MR parameters (resolution, contrast, filtering) can systematically bias automated brain MRI morphometry by 4-5%.
  • This bias is comparable to early Alzheimer disease-related brain volume changes.
  • Strict MR parameter selection or compensatory algorithms are needed in automated brain segmentation software to prevent parameter-related bias.