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Updated: Mar 15, 2026
High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
Published on: May 10, 2012
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.
Introduction:
Automated brain MRI morphometry, including hippocampal volumetry for Alzheimer disease, is increasingly recognized as a biomarker. Consequently, a rapidly increasing number of software tools have become available. We tested whether modifications of simple MR protocol parameters typically used in clinical routine systematically bias automated brain MRI segmentation results.
Methods:
The study was approved by the local ethical committee and included 20 consecutive patients (13 females, mean age 75.8 ± 13.8 years) undergoing clinical brain MRI at 1.5 T for workup of cognitive decline. We compared three 3D T1 magnetization prepared rapid gradient echo (MPRAGE) sequences with the following parameter settings: ADNI-2 1.2 mm iso-voxel, no image filtering, LOCAL- 1.0 mm iso-voxel no image filtering, LOCAL+ 1.0 mm iso-voxel with image edge enhancement. Brain segmentation was performed by two different and established analysis tools, FreeSurfer and MorphoBox, using standard parameters.
Results:
Spatial resolution (1.0 versus 1.2 mm iso-voxel) and modification in contrast resulted in relative estimated volume difference of up to 4.28 % (p < 0.001) in cortical gray matter and 4.16 % (p < 0.01) in hippocampus. Image data filtering resulted in estimated volume difference of up to 5.48 % (p < 0.05) in cortical gray matter.
Conclusion:
A simple change of MR parameters, notably spatial resolution, contrast, and filtering, may systematically bias results of automated brain MRI morphometry of up to 4-5 %. This is in the same range as early disease-related brain volume alterations, for example, in Alzheimer disease. Automated brain segmentation software packages should therefore require strict MR parameter selection or include compensatory algorithms to avoid MR parameter-related bias of brain morphometry results.
Insights
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.
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.
