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Related Experiment Videos

Probabilistic multi-site MR image harmonization via feature preserving conditional generative adversarial networks.

Saeed Moazami1, Sepideh Rezvani1, Agnimitra Dasgupta1

  • 1Aerospace and Mechanical Engineering, University of Southern California (USC), Los Angeles, CA, USA.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|May 22, 2026
PubMed
Summary

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This study introduces a novel deep learning method for harmonizing brain MRI scans from different sites. The feature-preserving conditional generative adversarial network (FP-cGAN) improves data consistency for multi-site neuroimaging studies.

Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Artificial Intelligence

Background:

  • Brain magnetic resonance imaging (MRI) is crucial for neurological disorder diagnosis.
  • Inconsistent image intensity distributions across different acquisition sites pose challenges for multi-site studies.
  • Existing image harmonization methods have limitations, especially in preserving anatomical features.

Purpose of the Study:

  • To develop a novel deep learning-based image harmonization technique for multi-site brain MRI.
  • To ensure anatomical feature preservation during image harmonization.
  • To integrate probabilistic generative models for improved harmonization.

Main Methods:

  • Introduced a feature preserving conditional generative adversarial network (FP-cGAN) with a novel regularizing constraint.
Keywords:
Generative adversarial networksGenerative modelsImage harmonizationMagnetic resonance imagingMedical imagingNeuroimaging

Related Experiment Videos

  • Trained models on unpaired MRI data and evaluated on paired (traveling subjects) data from the SRPBS dataset.
  • Compared FP-cGAN against histogram matching, ImUnity, and CycleGAN using distributional measures and heterogeneous datasets.

    • Main Results:

    • The proposed FP-cGAN method demonstrated superior performance compared to existing harmonization techniques.
    • The method effectively converted images from multiple origins to a target site format while preserving anatomical details.
    • Probabilistic formulation provided meaningful uncertainty estimates, analyzed through sample variance and uncertainty maps.

    Conclusions:

    • FP-cGAN offers a robust and generalizable solution for harmonizing multi-site brain MRI data.
    • The approach effectively addresses the challenge of intensity inconsistency in neuroimaging.
    • The integration of probabilistic generative models enhances harmonization quality and provides uncertainty quantification.