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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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Related Experiment Video

Updated: May 31, 2026

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

Temporal filtering effects in dynamic parallel MRI.

Martin Blaimer¹, Irene P Ponce, Felix A Breuer

¹Department of Diagnostic Imaging, Research Center Magnetic Resonance Bavaria (MRB), Würzburg, Germany. blaimer@mr-bavaria.de

Magnetic Resonance in Medicine

|June 23, 2011

Summary

This summary is machine-generated.

Autocalibrated parallel MRI methods can introduce aliasing artifacts in the direct current (DC) signal, causing temporal filtering issues. Filtering the DC signal significantly reduces these undesired effects in dynamic imaging.

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Area of Science:

Magnetic Resonance Imaging
Image Reconstruction
Signal Processing

Background:

Autocalibrated parallel MRI techniques like TSENSE and k-t SENSE enable high temporal resolution dynamic imaging.
These methods rely on temporal averaging of undersampled data to create an aliasing-free image, representing the direct current (DC) component for parameter derivation.

Purpose of the Study:

To investigate the presence and impact of aliasing artifacts within the DC signal in autocalibrated parallel MRI.
To demonstrate how these artifacts affect coil sensitivity calibration and data processing.
To propose and evaluate a method for mitigating these artifacts.

Main Methods:

Analysis of undersampled raw data to identify aliasing in the temporal average (DC) signal.
Simulation and experimental validation of artifact impact on coil calibration and data subtraction.
Application of DC signal filtering as a post-processing step.

Main Results:

Aliasing artifacts were confirmed to be present in the DC signal derived from undersampled k-t SENSE data.
These artifacts were shown to induce unwanted temporal filtering, distorting coil sensitivity maps and raw data.
Filtering the DC signal effectively reduced the observed temporal filtering effects.

Conclusions:

The direct current (DC) signal in autocalibrated parallel MRI is susceptible to aliasing artifacts.
These artifacts can compromise the accuracy of reconstruction parameters and introduce temporal distortions.
DC signal filtering is a viable strategy to mitigate these artifacts and improve image quality in dynamic MRI reconstruction.