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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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Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Published on: January 7, 2021

Deep-learning-based motion correction using multichannel MRI data: a study using simulated artifacts in the fastMRI

Miriam Hewlett^1,2, Ivailo Petrov¹, Patricia M Johnson³

¹Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada.

NMR in Biomedicine

|May 29, 2024

Summary

This summary is machine-generated.

Deep learning motion correction on individual MRI channel images significantly improves results over conventional methods. Applying deep learning before coil combination enhances image quality, potentially reducing the need for repeat scans.

Keywords:

MRI deep learning motion correction multichannel

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Area of Science:

Medical Imaging
Artificial Intelligence
Neuroscience

Background:

Deep learning offers motion correction for MRI without hardware changes.
Prior deep learning networks analyzed coil-combined data, not individual channels.
Multichannel MRI data's spatial encoding may aid motion correction.

Purpose of the Study:

To investigate deep learning for motion correction prior to coil combination in MRI.
To compare motion correction performance on single-channel vs. coil-combined data.
To evaluate a multichannel deep learning model for simultaneous motion correction.

Main Methods:

A conditional generative adversarial network was trained on simulated motion artifacts in brain MRI.
Performance was compared between single-channel, channel-combined, and multichannel deep learning models.
Data included multiple sites, contrasts, and subjects (healthy and non-healthy).

Main Results:

The single-channel model significantly improved mean absolute error by 50.9% (p<0.0001).
This outperformed the channel-combined model's 36.3% improvement (p<0.0001).
The multichannel model showed no significant image quality improvement.

Conclusions:

Motion correction on single-channel MRI data before coil combination enhances deep learning performance.
This approach is generalizable across sites and patient conditions.
Improved deep learning methods could reduce repeat MRI scans due to motion artifacts.