Deep learning for radial SMS myocardial perfusion reconstruction using the 3D residual booster U-net.

Journal: Magnetic resonance imaging
PMID: 34428512

Abstract

PURPOSE: To develop an end-to-end deep learning solution for quickly reconstructing radial simultaneous multi-slice (SMS) myocardial perfusion datasets with comparable quality to the pixel tracking spatiotemporal constrained reconstruction (PT-STCR) method.

Authors

  • Johnathan Le
    Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology and Imaging Sciences, University of Utah Salt Lake City, UT, USA; Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA.
  • Ye Tian
    State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics and Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, 361102, China.
  • Jason Mendes
    Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology and Imaging Sciences, University of Utah Salt Lake City, UT, USA.
  • Brent Wilson
    Department of Cardiology, University of Utah, Salt Lake City, UT, USA.
  • Mark Ibrahim
    Department of Cardiology, University of Utah, Salt Lake City, UT, USA.
  • Edward DiBella
    Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology and Imaging Sciences, University of Utah Salt Lake City, UT, USA; Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA.
  • Ganesh Adluru
    Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah.

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