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Updated: Jan 7, 2026

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

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Evaluation of Image-Level Harmonization Methods for Multi-Center MR Neuroimaging.

Brandon C Ho¹, Donghoon Kim¹, Ashwin Kumar¹

¹Department of Radiology, Stanford University, Stanford, California, USA.

Journal of Magnetic Resonance Imaging : JMRI

|January 5, 2026

Summary

This summary is machine-generated.

Deep learning harmonization (HACA3) improved MRI consistency across vendors more than statistical methods. However, harmonizing T2-FLAIR images remains challenging, indicating limitations in current multi-contrast MRI harmonization tools.

Keywords:

Alzheimer's disease harmonization neuroimaging scanner variability

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High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

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Area of Science:

Medical Imaging
Artificial Intelligence in Medicine
Neuroimaging

Background:

Multi-center imaging studies generate valuable data for pathology identification and deep learning model training.
Scanner and site variations can confound analyses, necessitating image harmonization.
Standardizing MRI data is crucial for reliable large-scale studies.

Purpose of the Study:

To assess scanner-induced differences in T1w and T2-FLAIR MRI scans within the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.
To evaluate the effectiveness of available image-level harmonization tools for MRI data.
To compare deep learning-based harmonization with traditional statistical methods.

Main Methods:

Retrospective analysis of 1143 ADNI3 subjects across different scanner vendors (GE, Philips, Siemens).
Comparison of gray/white matter contrast ratio (G/W ratio), white matter hyperintensity (WMH) volume, and image similarity metrics (FID, LPIPS) before and after harmonization.
Application of statistical (ComBat) and deep learning (HACA3) harmonization algorithms.

Main Results:

Significant baseline differences in G/W ratio and WMH volume were observed between scanner vendors.
Both ComBat and HACA3 improved G/W ratio consistency, with HACA3 showing superior performance.
HACA3 achieved the best image similarity across datasets and normalized WMH volume differences.
Harmonization of T2-FLAIR images, especially from GE scanners, showed improvement but still presented challenges.

Conclusions:

Deep learning-based HACA3 harmonization significantly outperformed statistical ComBat, enhancing MR contrast consistency and feature similarity across vendors.
While effective for T1w and some T2-FLAIR data, current harmonization tools face limitations in fully standardizing multi-contrast MRI data.
Further development is needed for robust harmonization of complex MRI contrasts like T2-FLAIR across diverse scanners.