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In vitro Assessment of Aortic Regurgitation Using Four-Dimensional Flow Magnetic Resonance Imaging
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Generalized super-resolution 4D Flow MRI-using ensemble learning to extend across the cardiovascular system.

Leon Ericsson1, Adam Hjalmarsson1, Muhammad Usman Akbar1

  • 1L.E., A.H., A.F., A.S., and D.M. are with Karolinska Institutet, Solna, Sweden. M.U.A. is with Linköping University, Linköping, Sweden. E.F. and A.A.Y. are with the University of Auckland, Auckland, New Zealand. M.B., B.H, N.B, C.A.F, and D.A.N. are with the University of Michigan, Ann Arbor, USA. J.S. is with the University of California San Francisco, San Francisco, CA, USA. M.A. ans S.S. are with Northwestern University, Chicago, USA. S.S. is also with the University of Greifswald, Germany. A.A.Y. is also with King's College London, London, UK. D.M. is also with Massachusetts Institute of Technology, Cambridge, USA.

Arxiv
|December 4, 2023
PubMed
Summary

Super-resolution (SR) networks enhance 4D Flow MRI quality. Ensemble learning with heterogeneous data generalizes SR across cardiac, aortic, and cerebrovascular domains for improved blood flow quantification.

Keywords:
4D Flow MRIcardiovascularensemble learninghemodynamicssuper-resolution

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Area of Science:

  • Cardiovascular Imaging
  • Medical Image Analysis
  • Artificial Intelligence in Medicine

Background:

  • 4D Flow MRI quantifies blood flow non-invasively but is limited by spatial resolution and noise.
  • Super-resolution (SR) networks can improve 4D Flow MRI image quality post-scan.
  • Current SR applications are domain-specific, limiting generalizability across diverse cardiovascular hemodynamics.

Conclusions:

  • Ensemble learning with heterogeneous training data provides a viable approach for generalized SR 4D Flow MRI.
  • This method extends the utility of SR across diverse cardiovascular applications, including cardiac, aortic, and cerebrovascular imaging.
  • The developed approach enhances blood flow quantification and image quality in 4D Flow MRI.