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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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Accelerated Cine Cardiac MRI Using Deep Learning-Based Reconstruction: A Systematic Evaluation.

Amol Pednekar1,2, Murat Kocaoglu1,2, Hui Wang1,2,3

  • 1Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.

Journal of Magnetic Resonance Imaging : JMRI
|October 19, 2023
PubMed
Summary
This summary is machine-generated.

Deep learning reconstruction (DLR) of undersampled cardiac MRI can maintain diagnostic accuracy for biventricular volumes even with significant acceleration. This technique allows for shorter breath-holds, improving patient tolerance and scan efficiency.

Keywords:
adaptive intelligencebiventricular volumecardiac MRIcine imagingcompressed SENSE

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Area of Science:

  • Cardiovascular Imaging
  • Medical Physics
  • Artificial Intelligence in Medicine

Background:

  • Breath-holding (BH) for cine balanced steady state free precession (bSSFP) imaging poses challenges for patients with limited BH capacity.
  • Deep learning-based reconstruction (DLR) offers a potential solution by enabling faster imaging with undersampled k-space data.
  • DLR aims to preserve image quality and accuracy in ventricular assessment during cardiac MRI.

Purpose of the Study:

  • To systematically evaluate the performance of DLR for cine bSSFP images acquired with varying degrees of k-space undersampling.
  • To assess the impact of different acceleration factors on image quality and diagnostic adequacy.
  • To determine the maximum acceleration factor at which DLR maintains reliable ventricular volume measurements.

Main Methods:

  • Retrospective analysis of cine bSSFP data from 15 pectus excavatum patients.
  • Systematic undersampling of k-space using compressed sensitivity encoding (C-SENSE) with acceleration factors (R) ranging from 2 to 8.
  • Quantitative image quality assessment using structural similarity index measure (SSIM) and qualitative evaluation of contrast and edge definition by experienced readers.
  • Automated biventricular segmentation using commercial software.

Main Results:

  • A significant decrease in image quality and edge definition was observed as the acceleration factor (R) increased.
  • Diagnostically adequate image quality was maintained up to an acceleration factor of R=5.
  • No significant effect of acceleration factor on biventricular volumetric indices was found (P=0.447).

Conclusions:

  • Deep learning reconstruction of undersampled cine bSSFP data is comparable to fully sampled data for biventricular volume assessment up to an acceleration factor of R=5.
  • DLR holds promise for reducing breath-hold duration in cardiac MRI without compromising volumetric accuracy.
  • This approach can improve patient comfort and scan efficiency in cardiac MRI examinations.