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Related Concept Videos

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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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: Jun 26, 2025

Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging
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Motion-robust free-running volumetric cardiovascular MRI.

Syed M Arshad1,2, Lee C Potter2,3, Chong Chen1,2

  • 1Biomedical Engineering, The Ohio State University, Columbus, Ohio, USA.

Magnetic Resonance in Medicine
|May 11, 2024
PubMed
Summary
This summary is machine-generated.

A new method, compressive recovery with outlier rejection (CORe), improves cardiovascular MRI (CMR) by reducing motion artifacts. CORe enhances image quality and flow measurements in free-running scans.

Keywords:
MRI reconstructioncardiac imagingflow imagingmotion artifactoutlier rejection

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Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Cardiovascular Research

Background:

  • Free-running volumetric cardiovascular MRI (CMR) is susceptible to motion artifacts.
  • Robustness to motion is crucial for accurate CMR image acquisition and analysis.
  • Existing methods may not fully address outlier data caused by motion.

Purpose of the Study:

  • To introduce and evaluate a novel outlier mitigation technique for free-running volumetric CMR.
  • To enhance the robustness of cardiovascular MRI to motion-induced artifacts.
  • To improve the reliability of quantitative measurements derived from CMR data.

Main Methods:

  • The compressive recovery with outlier rejection (CORe) method models outliers as an additive variable.
  • MR physics-guided group sparsity is enforced on the auxiliary variable.
  • Joint estimation of the image and auxiliary variable is performed iteratively.
  • CORe was compared against traditional compressed sensing (CS), robust regression (RR), and an existing outlier rejection method in simulations and real CMR datasets.

Main Results:

  • CORe demonstrated superior performance over CS, RR, and existing methods in simulation studies.
  • Expert evaluation confirmed CORe's effectiveness in artifact suppression and maintaining image sharpness for 3D cine CMR.
  • CORe provided more reliable and consistent 4D flow measurements, particularly under motion and stress conditions.

Conclusions:

  • The presented outlier rejection method, CORe, effectively suppresses motion artifacts in free-running CMR.
  • CORe shows significant potential for improving image quality and quantitative analysis across various free-running CMR applications.
  • This method enhances the utility of cardiovascular MRI in challenging acquisition scenarios.