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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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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

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Published on: July 28, 2013

Data-Driven Regularization Parameter Selection in Dynamic MRI.

Matti Hanhela¹, Olli Gröhn², Mikko Kettunen²

¹Department of Applied Physics, University of Eastern Finland, 70211 Kuopio, Finland.

Journal of Imaging

|August 30, 2021

Summary

This summary is machine-generated.

This study introduces a data-driven method for selecting regularization parameters in compressed sensing (CS) MRI reconstruction. The approach ensures optimal balance between data fidelity and regularization, improving image quality in dynamic MRI.

Keywords:

S-curve compressed sensing dynamic MRI parameter selection regularization parameter

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Area of Science:

Medical Imaging
Magnetic Resonance Imaging
Image Reconstruction

Background:

Dynamic MRI requires high temporal resolution, often achieved through undersampled data acquisition.
Compressed sensing (CS) is popular for reconstructing undersampled dynamic MRI data.
Determining appropriate regularization parameters in CS is crucial for balancing data fidelity and image regularization.

Purpose of the Study:

To propose a data-driven approach for selecting total variation regularization parameters in CS dynamic MRI.
To enable reconstructions that achieve expected sparsity levels in regularization domains.
To evaluate two proposed formulations: S-surface and Sequential S-curve parameter selection.

Main Methods:

Developed a data-driven method for total variation regularization parameter selection.
Utilized measurement data for temporal regularization and a reference image for spatial regularization to determine expected sparsity levels.
Proposed simultaneous (S-surface) and sequential (Sequential S-curve) parameter selection strategies.

Main Results:

Both proposed methods yielded parameter pairs and reconstructions close to the RMSE-optimal in simulated DCE-MRI data.
In experimental DCE-MRI, both methods selected similar parameters, resulting in high-quality reconstructions.
The methods demonstrated feasible regularization parameter selection in both simulated and experimental scenarios.

Conclusions:

The proposed data-driven methods effectively select regularization parameters for CS dynamic MRI.
The sequential method offers computational efficiency while maintaining reconstruction quality.
These approaches enhance the feasibility and quality of dynamic MRI reconstructions using compressed sensing.