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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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Author Spotlight: Noninvasive Cerebral Blood Flow Determination in Human Functional Brain Region for Diagnosis of Neurological Disorders

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Rapid 3D dynamic arterial spin labeling with a sparse model-based image reconstruction.

Li Zhao¹, Samuel W Fielden¹, Xue Feng¹

¹Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.

|July 15, 2015

Summary

Accelerated dynamic arterial spin labeling (ASL) MRI uses model-based reconstruction for faster, more accurate brain blood flow measurements. This technique improves signal-to-noise ratio and motion robustness, enabling quicker scans.

Keywords:

Arterial spin labeling Brain perfusion MRI Compressed sensing Dynamic ASL Model-based sparsity Single-shot spiral ASL

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Area of Science:

Magnetic Resonance Imaging (MRI)
Neuroimaging
Biomedical Engineering

Background:

Dynamic arterial spin labeling (ASL) MRI accurately estimates cerebral blood flow by tracking perfusion over time.
Traditional ASL MRI suffers from low signal-to-noise ratio (SNR) and motion sensitivity, necessitating lengthy scan times.
Existing methods require extensive signal averaging, limiting clinical applicability.

Purpose of the Study:

To develop an accelerated dynamic ASL method using model-based image reconstruction.
To enhance SNR and motion robustness in dynamic ASL imaging.
To reduce overall scan times for dynamic ASL acquisitions.

Main Methods:

Employed a single-shot 3D turbo spin echo spiral pulse sequence accelerated with parallel imaging and compressed sensing.
Integrated the pulse sequence into a dynamic pseudo-continuous ASL acquisition at multiple time points.
Utilized joint image reconstruction with a model of perfusion time courses and sparsity constraints.

Main Results:

Numerical phantom simulations demonstrated improved SNR and reduced estimation errors with spatial sparsity.
Model-based sparsity further enhanced SNR, decreased errors, and suppressed motion artifacts.
Experimental validation on volunteers showed significant improvements, achieving scan times as short as 20 seconds per time point.

Conclusions:

Model-based image reconstruction enables rapid dynamic ASL with improved accuracy.
The developed method offers enhanced robustness to motion artifacts.
This technique holds promise for more efficient and reliable neuroimaging of cerebral blood flow.