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Pathology-preserving intensity standardization framework for multi-institutional FLAIR MRI datasets

Brittany Reiche1, A R Moody2, April Khademi3

  • 1School of Engineering, University of Guelph, Guelph, Canada.

Magnetic Resonance Imaging
|May 19, 2019
PubMed

Insights

Multi-centre studies analyzing brain white matter lesions (WML) using Fluid-Attenuated Inversion Recovery (FLAIR) MRI face image variability. This study introduces an intensity standardization framework to reduce this multi-centre effect, improving automated analysis.

Area of Science:

  • Medical Imaging
  • Neuroimaging
  • Radiology

Background:

  • Fluid-Attenuated Inversion Recovery (FLAIR) MRI is crucial for analyzing brain white matter lesions (WML) associated with neurodegenerative diseases.
  • Multi-centre (MC) studies are essential for understanding disease progression but suffer from image variability due to diverse acquisition parameters.
  • This variability, termed the MC effect, poses significant challenges for automated image analysis algorithms.

Purpose of the Study:

  • To investigate the variability in FLAIR MRI image properties across different institutions and scanner vendors in multi-centre studies.
  • To propose and evaluate an intensity standardization framework to mitigate the MC effect in FLAIR MRI.
  • To demonstrate the impact of intensity standardization on the performance of automated algorithms, such as brain extraction.

Main Methods:

  • Analysis of approximately 5000 multi-centre FLAIR MRI volumes to characterize image property variability.
  • Development and application of an intensity standardization framework designed to normalize FLAIR MRI intensities while preserving white matter lesion appearance.
  • Implementation and comparison of a threshold-based brain extraction algorithm against a classifier-based approach on standardized and original data.

Main Results:

  • Significant variations in image characteristics were observed across different scanner vendors and centres in the original FLAIR MRI data.
  • The proposed intensity standardization framework effectively reduced the observed variability between centres and vendors.
  • The threshold-based brain extraction algorithm achieved a competitive Dice Similarity Coefficient of 81% on 183 volumes after intensity standardization.

Conclusions:

  • Intensity non-standardness in multi-centre FLAIR MRI studies is a significant issue that impacts automated analysis.
  • The developed intensity standardization framework successfully reduces multi-centre variability, enabling more robust and simplified automated algorithms.
  • Optimized pre-processing through intensity standardization enhances the reliability of large-scale neuroimaging studies for analyzing white matter lesions.
Keywords:
Alzheimer's diseaseBrainFluid-attenuated inversion recoveryIntensity standardizationSegmentationVascular diseaseWhite matter lesions

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