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MISPEL: A supervised deep learning harmonization method for multi-scanner neuroimaging data.

Mahbaneh Eshaghzadeh Torbati1, Davneet S Minhas2, Charles M Laymon3

  • 1Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA.

Medical Image Analysis
|August 18, 2023
PubMed
Summary
This summary is machine-generated.

Large-scale neuroimaging data aggregation faces challenges from scanner variability. MISPEL (Multi-scanner Image harmonization via Structure Preserving Embedding Learning) effectively harmonizes multi-scanner data, improving analysis reliability.

Keywords:
HarmonizationMRINormalizationScanner effectsTechnical variability

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Area of Science:

  • Neuroimaging
  • Data Science
  • Medical Imaging Analysis

Background:

  • Aggregated multi-site neuroimaging datasets offer increased statistical power.
  • Scanner specification differences introduce technical variability, potentially biasing analyses.
  • Effective data normalization and harmonization are crucial for reliable multi-site studies.

Purpose of the Study:

  • To propose MISPEL (Multi-scanner Image harmonization via Structure Preserving Embedding Learning), a novel supervised harmonization method.
  • To develop criteria for assessing scanner-related variability and evaluating harmonization techniques.
  • To address technical variability in aggregated neuroimaging data.

Main Methods:

  • Developed MISPEL, a supervised multi-scanner harmonization method.
  • Created a multi-scanner matched dataset of 3T T1 images across four scanners.
  • Evaluated harmonization using FSL and SPM segmentation frameworks.

Main Results:

  • MISPEL demonstrated superior performance compared to White Stripe, RAVEL, and CALAMITI.
  • The proposed criteria effectively investigated scanner-related variability.
  • MISPEL showed promise for various neuroimaging modalities.

Conclusions:

  • MISPEL is an effective and extendable solution for multi-scanner neuroimaging data harmonization.
  • The developed evaluation criteria provide a robust framework for assessing harmonization techniques.
  • Addressing scanner variability is essential for maximizing the benefits of large-scale neuroimaging datasets.