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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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Interleaved diffusion-weighted improved by adaptive partial-Fourier and multiband multiplexed sensitivity-encoding

Hing-Chiu Chang¹, Shayan Guhaniyogi, Nan-Kuei Chen

¹Brain Imaging and Analysis Center, Duke University Medical Center, Durham, North Carolina, USA.

Magnetic Resonance in Medicine

|June 14, 2014

Summary

This summary is machine-generated.

New techniques eliminate artifacts in diffusion-weighted imaging (DWI) using multiplexed sensitivity encoding (MUSE) and adaptive Homodyne reconstruction. This improves image quality and scan speed for research and clinical applications.

Keywords:

artifact correction diffusion-weighted imaging echo-planar imaging multiplexed sensitivity encoding

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Diffusion Imaging in the Rat Cervical Spinal Cord

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Area of Science:

Medical Imaging
Neuroimaging

Background:

Interleaved echo-planar imaging (EPI) based diffusion-weighted imaging (DWI) is prone to artifacts.
Existing reconstruction methods may not fully eliminate these artifacts, particularly those caused by motion or magnetic field gradients.

Purpose of the Study:

To develop and present reliable techniques for artifact elimination in interleaved EPI-based DWI.
To enhance image quality, resolution, and scan throughput for DWI.

Main Methods:

Integration of the multiplexed sensitivity encoding (MUSE) algorithm with an adaptive Homodyne partial-Fourier reconstruction algorithm.
Generalization of the single-band MUSE framework to multiband MUSE to address aliasing artifacts in multiband multishot interleaved DWI data.

Main Results:

The adaptive Homodyne-MUSE reconstruction algorithm effectively eliminates residual artifacts, producing high-quality, high-resolution DWI.
The generalized MUSE algorithm is compatible with multiband and high-throughput DWI acquisition schemes.

Conclusions:

The combined multiband and adaptive Homodyne-MUSE algorithms significantly improve spatial resolution, image quality, and scan throughput of interleaved DWI.
This reconstruction framework is expected to be crucial for high-resolution DWI in both neuroscience research and clinical settings.