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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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Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

Submillimeter MR fingerprinting using deep learning-based tissue quantification.

Zhenghan Fang^1,2,3, Yong Chen^1,2, Sheng-Che Hung^1,2

¹Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.

Magnetic Resonance in Medicine

|December 20, 2019

Summary

This summary is machine-generated.

A new deep learning method enables rapid, high-resolution brain MRI scans for T1 and T2 quantification. This technique achieves submillimeter resolution in just 7.5 seconds, improving accuracy and detail in tissue mapping.

Keywords:

MR fingerprinting deep learning pediatric imaging quantitative imaging

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Area of Science:

Magnetic Resonance Imaging (MRI)
Medical Imaging
Artificial Intelligence in Medicine

Background:

Magnetic Resonance (MR) fingerprinting enables quantitative tissue property mapping.
Achieving high-resolution and rapid MR fingerprinting remains a challenge.
Deep learning offers potential for accelerating and enhancing MR imaging analysis.

Purpose of the Study:

To develop a rapid 2D MR fingerprinting technique with submillimeter in-plane resolution.
To utilize a deep learning-based approach for improved tissue quantification.
To enhance the accuracy and detail of T1 and T2 mapping in the brain.

Main Methods:

Developed a 2D MR fingerprinting sequence with a spiral trajectory for 0.8-mm in-plane resolution.
Employed a novel residual channel attention U-Net deep learning architecture for tissue characterization.
Replaced standard template matching with the deep learning method for improved tissue property estimation.
Acquired quantitative brain MR images from adult and pediatric subjects.

Main Results:

Achieved high-quality T1 and T2 mapping with 0.8-mm in-plane resolution in 7.5 seconds per slice.
Demonstrated superior accuracy of the deep learning method compared to existing algorithms.
Showcased improved preservation of high-resolution details in brain tissues using the proposed U-Net architecture.
Validated the technique's potential for fast pediatric neuroimaging with accelerated tissue property mapping.

Conclusions:

A rapid and high-resolution MR fingerprinting technique was successfully developed.
The method enables high-quality T1 and T2 quantification with submillimeter resolution in under 10 seconds.
Deep learning significantly enhances the speed and accuracy of quantitative MR imaging.