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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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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Disentangled latent energy-based style translation: An image-level structural MRI harmonization framework.

Mengqi Wu¹, Lintao Zhang², Pew-Thian Yap²

¹Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC 27599, USA.

Neural Networks : the Official Journal of the International Neural Network Society

|December 19, 2024

Summary

This study introduces a new method, DLEST, to harmonize brain MRI scans, reducing variations from different scanners. DLEST efficiently corrects site effects for better image analysis and synthesis.

Keywords:

Energy-based model MRI harmonization MRI synthesis Style translation

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Area of Science:

Medical Imaging
Neuroscience
Computer Vision

Background:

Brain magnetic resonance imaging (MRI) is crucial in research and clinical settings.
Site effects from varying scanners and field strengths introduce non-biological variations.
Existing MRI harmonization methods are computationally intensive and lack generalizability.

Purpose of the Study:

To develop a novel, efficient, and generalizable MRI harmonization framework.
To address limitations of current retrospective MRI harmonization techniques.
To improve the reliability and applicability of MRI data across different sites.

Main Methods:

Introduced a Disentangled Latent Energy-Based Style Translation (DLEST) framework.
Employed site-invariant image generation (SIG) using a latent autoencoder.
Utilized site-specific style translation (SST) with an energy-based model and site-specific MRI synthesis (SMS).
Disentangled image generation and style translation in latent space for efficiency.

Main Results:

DLEST demonstrated superior performance over state-of-the-art methods.
Validated on T1-weighted MRIs from a large public dataset (3,984 subjects, 58 sites).
Performance confirmed through histogram/feature visualization, site classification, tissue segmentation, and synthesis tasks.
Showcased effectiveness on an independent dataset with traveling subjects across 11 sites.

Conclusions:

DLEST offers an efficient and generalizable solution for unpaired MRI harmonization.
The disentangled approach in latent space significantly improves style translation.
The framework enhances the utility of multi-site MRI data for research and clinical applications.