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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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Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging
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Ferumoxytol-Enhanced Cardiac Cine MRI Reconstruction Using a Variable-Splitting Spatiotemporal Network.

Chang Gao1,2, Zhengyang Ming1,2, Kim-Lien Nguyen1,2,3

  • 1Department of Physics and Biology in Medicine, University of California Los Angeles, Los Angeles, California, USA.

Journal of Magnetic Resonance Imaging : JMRI
|March 4, 2024
PubMed
Summary
This summary is machine-generated.

A new deep learning network, VSNet, was developed for cardiac MRI reconstruction. It shows superior image quality and accurate functional measurements for Ferumoxytol-enhanced gradient echo cine imaging.

Keywords:
cardiac GRE cine MRIcongenital heart diseasedeep learningferumoxytolimage reconstruction

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Radiology
  • Cardiac MRI

Background:

  • Balanced steady-state free precession (bSSFP) is standard for cardiac cine MRI but suffers from artifacts.
  • Ferumoxytol-enhanced (FE) gradient echo (GRE) offers an alternative, necessitating efficient reconstruction methods.
  • Leveraging existing bSSFP data can improve FE GRE cine imaging through advanced network development.

Purpose of the Study:

  • To develop a variable-splitting spatiotemporal network (VSNet) for cardiac MRI image reconstruction.
  • To train VSNet on bSSFP cine images and ensure its applicability to FE GRE cine images.
  • To create a computationally efficient network for improved cardiac MRI.

Main Methods:

  • Retrospective and prospective study design.
  • Network training on 41 patients' bSSFP cine images; testing on 31 patients and 5 healthy subjects' FE GRE cine images.
  • Comparison of VSNet against other reconstruction methods (total variation loss, compressed sensing, low rank) at 14x acceleration, using GRAPPA images as reference.

Main Results:

  • VSNet significantly outperformed other methods in Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), qualitative ranking, and latent scores.
  • Quantitative analysis showed VSNet achieved comparable left ventricular (LV) and right ventricular (RV) end-systolic volume (ESV) and ejection fraction (EF) to the reference.
  • VSNet demonstrated a statistically significant but small difference in end-diastolic volume (EDV) compared to the reference.

Conclusions:

  • VSNet achieved superior image quality and more accurate functional measurements for FE GRE cine images compared to other tested 14x accelerated reconstruction methods.
  • The developed VSNet offers a promising solution for artifact reduction and improved diagnostic accuracy in cardiac MRI.
  • This study highlights the potential of deep learning for enhancing MRI techniques.