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Real-time Video Projection in an MRI for Characterization of Neural Correlates Associated with Mirror Therapy for Phantom Limb Pain

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Extrapolation and correlation (EXTRACT): a new method for motion compensation in MRI.

Wei Lin¹, Hee Kwon Song

¹Laboratory for Structural NMR Imaging, Departmentof Radiology, University of Pennsylvania, Philadelphia, PA 19104 USA. wlin@invivocorp.com

IEEE Transactions on Medical Imaging

|January 1, 2009

Summary

This study introduces a novel postprocessing technique to correct motion artifacts in magnetic resonance imaging (MRI). The method uses k-space extrapolation to generate a motion-free reference, improving image quality.

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Area of Science:

Medical Imaging
Biophysics
Signal Processing

Background:

Motion artifacts significantly degrade the quality of magnetic resonance imaging (MRI) data.
Accurate motion correction is crucial for reliable diagnosis and research using MRI.

Purpose of the Study:

To develop and validate a postprocessing technique for correcting translational and rotational motion artifacts in MRI.
To investigate and combine different k-space extrapolation methods for robust motion estimation.

Main Methods:

A two-step postprocessing approach: k-space extrapolation for a motion-free reference and correlation for motion estimation.
Evaluation of two k-space extrapolation techniques: edge enhancement and finite-support solution.
Combination of extrapolation methods to optimize motion-free reference generation for accurate motion detection.

Main Results:

Finite-support solution excels in k-space center extrapolation, while edge enhancement is superior in outer regions.
A combined approach effectively generates a motion-free reference for accurate motion estimation.
The proposed technique demonstrated successful motion compensation in simulations and in vivo MR experiments.

Conclusions:

The developed postprocessing technique effectively corrects translational and rotational motion artifacts in MRI.
The combined k-space extrapolation method provides a robust solution for motion estimation.
The technique is resilient to noise and various motion types, enhancing MRI data reliability.