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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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Related Experiment Video

Updated: Nov 12, 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

Published on: April 14, 2014

MRI restoration using edge-guided adversarial learning.

Yaqiong Chai^1,2, Botian Xu¹, Kangning Zhang³

¹Department of Biomedical Engineering, University of Southern California, CA, USA.

IEEE Access : Practical Innovations, Open Solutions

|March 22, 2021

Summary

This summary is machine-generated.

This study introduces an edge-guided generative adversarial network (GAN) to restore missing slices and artifacts in 2D brain MRI scans. The novel method improves image quality for 3D reconstruction and comparative studies.

Keywords:

artifact correction edge generative adversarial network image restoration imputation magnetic resonance imaging

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Published on: November 23, 2019

Area of Science:

Medical Imaging
Artificial Intelligence
Neuroscience

Background:

Multislice 2D MRI data present challenges for 3D reconstruction due to sparse through-plane sampling.
Image imputation techniques are crucial for restoring missing data or correcting artifacts in MRI scans.

Purpose of the Study:

To develop an edge-guided generative adversarial network (GAN) for restoring missing through-plane slices and artifacts in brain MRI images.
To enhance the utility of 2D MRI scans for 3D applications and large-scale brain morphometry studies.

Main Methods:

Proposed an edge-guided GAN inspired by image inpainting, decoupling restoration into edge connection and contrast completion stages.
Trained and tested the network on Human Connectome Project data for thick slice imputation and on clinical/simulated data for artifact correction.

Main Results:

The Edge-Guided GAN demonstrated superior performance in Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), conspicuity, and signal texture.
Outperformed traditional imputation tools like Context Encoder and Densely Connected Super Resolution Network with GAN (DCSRN-GAN).

Conclusions:

The proposed GAN effectively restores damaged brain MRI images, addressing limitations of 2D acquisition.
This method can enhance the utilization of clinical 2D MRI scans for generating 3D atlases and conducting big-data comparative morphometry studies.