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Related Experiment Video

Updated: Oct 14, 2025

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

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A multi-scanner neuroimaging data harmonization using RAVEL and ComBat.

Mahbaneh Eshaghzadeh Torbati¹, Davneet S Minhas², Ghasan Ahmad³

¹Intelligent System Program, University of Pittsburgh School of Computing and Information, Pittsburgh, PA 15213, USA.

|November 5, 2021

Summary

This summary is machine-generated.

Data harmonization methods like ComBat improve neuroimaging analysis across different scanners. ComBat offers more consistent regional harmonization than RAVEL, reducing bias and variance in Alzheimer

Keywords:

Alzheimer's disease Harmonization MRI Normalization Scanner effects

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Area of Science:

Neuroimaging
Medical Image Analysis
Biostatistics

Background:

Neuroimaging studies often combine data from multiple scanners and conditions.
Technical variability, including image intensity scale and scanner effects, can bias results.
Standardized data analysis is crucial for reliable neuroimaging research.

Purpose of the Study:

To evaluate and compare data analysis methods for neuroimaging data harmonization.
To assess the effectiveness of intensity normalization (RAVEL) and regional harmonization (ComBat).
To determine the optimal method for reducing technical variability in multi-scanner neuroimaging studies.

Main Methods:

Comparison of data analysis methods: RAW (no transformation), RAVEL (intensity normalization), ComBat (regional harmonization), and RAVEL-ComBat combination.
Evaluation on data from 16 participants scanned on 1.5T and 3T scanners.
Neuroradiological evaluation of 7 Alzheimer's disease-relevant regions of interest (ROIs).

Main Results:

RAVEL significantly improved image intensity reproducibility.
ComBat demonstrated superior regional harmonization consistency across subjects and measures compared to RAVEL and RAVEL-ComBat.
Both RAVEL and ComBat reduced bias compared to RAW data, but RAVEL increased variance.
ComBat exhibited a lower root mean square deviation (RMSD) than RAVEL due to lower variance.

Conclusions:

ComBat is the preferred method for regional harmonization in multi-scanner neuroimaging studies.
Data harmonization techniques like ComBat are essential for mitigating scanner effects and improving data reproducibility.
These findings support the use of ComBat for enhancing the reliability of neuroimaging analyses in Alzheimer's disease research.