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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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Using the robust principal component analysis algorithm to remove RF spike artifacts from MR images.

Adrienne E Campbell-Washburn^1,2, David Atkinson³, Zoltan Nagy^4,5

¹Centre for Advanced Biomedical Imaging, University College London, London, United Kingdom.

Magnetic Resonance in Medicine

|July 21, 2015

Summary

This summary is machine-generated.

Robust Principal Component Analysis effectively removes radiofrequency noise spikes from MRI k-space data, preventing disruptive image artifacts. This method preserves underlying signals, offering a valuable postprocessing solution for improved image quality.

Keywords:

robust principal component analysis spike noise stripe artifact

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Area of Science:

Magnetic Resonance Imaging (MRI)
Medical Image Processing
Signal Processing

Background:

Radiofrequency (RF) noise during MRI acquisition causes "k-space spikes," leading to image artifacts like stripes.
These spikes are often linked to hardware issues, particularly during gradient-intensive sequences such as diffusion-weighted imaging.

Purpose of the Study:

To present and evaluate the Robust Principal Component Analysis (RPCA) algorithm for removing spike noise from k-space data.
To demonstrate the effectiveness of RPCA in mitigating stripe artifacts in various MRI data types.

Main Methods:

RPCA was employed to decompose corrupted k-space matrices into low-rank (artifact-free) and sparse (spike) components.
Automated center refilling was utilized to prevent misclassification of the central k-space region.

Main Results:

The RPCA algorithm successfully removed k-space spikes across four distinct datasets: mouse heart T1 mapping, mouse heart cine imaging, human kidney diffusion tensor imaging (DTI), and human brain DTI.
Post-despiking analysis showed minimal impact on the underlying signal, with myocardial T1 values changing by 86.1 ± 171 ms and fractional anisotropy values being recovered in DTI data.

Conclusions:

The RPCA despiking algorithm is an effective postprocessing technique for retrospectively removing stripe artifacts caused by k-space spikes.
This method preserves the signal of interest, making it a valuable tool for enhancing MRI image quality without altering original data.