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Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Updated: Mar 13, 2026

Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring

Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring

Published on: December 9, 2010

Harmonizing Diffusion MRI Data Across Multiple Sites and Scanners.

Hengameh Mirzaalian¹, Amicie de Pierrefeu¹, Peter Savadjiev¹

¹Harvard Medical School and Brigham and Women's Hospital, Boston, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

|October 19, 2016

Summary

This summary is machine-generated.

This study presents a novel, model-free method to harmonize diffusion MRI data across sites and scanners. It accurately removes scanner-specific differences, improving neuroimaging analysis by accounting for spatial signal variability.

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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Area of Science:

Neuroimaging
Medical Physics
Biomedical Engineering

Background:

Harmonizing diffusion MRI (dMRI) data across multiple sites is crucial for large-scale neuroimaging studies.
Existing methods often fail to account for spatial variability in signal differences across the brain.
Scanner-related variations can introduce biases in diffusion MRI data analysis.

Purpose of the Study:

To develop a novel, model-free method for harmonizing dMRI data across different sites and scanners.
To address the limitations of existing methods by considering spatial signal variability.
To enable more robust and powerful joint analysis of multi-site dMRI datasets.

Main Methods:

Utilized spherical harmonic basis functions to represent dMRI signals.
Computed rotation-invariant features to estimate site-specific linear mappings.
Developed a regionally specific signal correction approach, avoiding a priori diffusion models.

Main Results:

Validated the method on dMRI data from four different sites and scanners (GE and Siemens).
Demonstrated accurate removal of scanner-specific differences for identical acquisition protocols.
Showed significant improvements in harmonizing diffusion measures like fractional anisotropy and mean diffusivity.

Conclusions:

The proposed model-free method effectively harmonizes dMRI data across sites and scanners.
Accounting for spatial signal variability enhances the accuracy of multi-site dMRI analysis.
This approach facilitates increased sample sizes and statistical power in neuroimaging research.