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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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Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla

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Improved susceptibility weighted imaging method using multi-echo acquisition.

Sung Suk Oh¹, Se-Hong Oh, Yoonho Nam

¹Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA; Department of Electrical Engineering, KAIST, Daejeon, Korea.

Magnetic Resonance in Medicine

|October 10, 2013

Summary

This summary is machine-generated.

This study introduces a new method for susceptibility weighted imaging (SWI) to reduce artifacts. The novel approach enhances visualization of brain structures, particularly in the frontal lobe.

Keywords:

Gaussian modeling removal compensation gradient field inhomogeneity induced artifact magnetic susceptibility z-shim

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Area of Science:

Medical Imaging
Neuroimaging

Background:

Susceptibility weighted imaging (SWI) is sensitive to magnetic field inhomogeneities.
Background field inhomogeneity artifacts degrade SWI image quality in both magnitude and phase data.

Purpose of the Study:

To develop novel acquisition and postprocessing techniques for SWI.
To effectively remove background field inhomogeneity artifacts in SWI magnitude and phase data.

Main Methods:

Acquisition of three echoes using a 3D gradient echo (GRE) sequence.
Application of a z-shim gradient to the third echo for field compensation.
Artifact compensation in magnitude data via signal estimation from all three echoes.
Phase artifact removal using Gaussian modeling of background phase distortions.

Main Results:

Successful compensation of background field inhomogeneity artifacts in magnitude and phase images.
Demonstrated significant improvement in SWI image quality.
Identification of previously unobserved vessels in the frontal lobe compared to conventional SWI.

Conclusions:

The novel method enhances SWI image quality.
Restoration of signal in frontal and temporal regions improves diagnostic utility.