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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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Updated: Jun 30, 2026

Functional Mapping with Simultaneous MEG and EEG

Functional Mapping with Simultaneous MEG and EEG

Published on: June 14, 2010

Regularized field map estimation in MRI.

Amanda K Funai¹, Jeffrey A Fessler, Desmond T B Yeo

¹Department of Electrical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.

IEEE Transactions on Medical Imaging

|September 26, 2008

Summary

This summary is machine-generated.

Accurate field maps are crucial for correcting magnetic resonance (MR) imaging distortions. Regularized methods significantly improve field map quality, especially in low-density regions, enhancing image clarity.

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Area of Science:

Medical Imaging
Biophysics
Image Processing

Background:

Fast magnetic resonance (MR) imaging techniques like echo-planar imaging (EPI) and spiral scans are susceptible to image distortion and blurring due to field inhomogeneity.
Accurate field maps, representing off-resonance frequencies, are essential for correcting these artifacts.
Conventional field map estimation methods often produce noisy results, particularly in low spin density areas.

Purpose of the Study:

To introduce and evaluate regularized methods for improved field map estimation in MR imaging.
To address the limitations of standard field map estimation techniques in regions with low spin density.

Main Methods:

Development of regularized methods for field map estimation using two or more MR scans with varying echo times.
Application of algorithms that monotonically decrease a regularized least-squares cost function to handle the nonlinear problem.
Exploitation of the inherent smoothness property of field maps.

Main Results:

The proposed regularized methods demonstrate a significant improvement in the quality of estimated field maps compared to unregularized approaches.
Enhanced accuracy in field map estimation, particularly in challenging image regions with low spin density.
Reduction in image distortion and blurring in fast MR imaging sequences.

Conclusions:

Regularized field map estimation offers a robust solution for improving image quality in fast MR imaging.
These methods provide more accurate field maps, crucial for reliable image reconstruction and analysis.
The developed techniques are valuable for applications requiring high-fidelity MR images.