Improved quantitative parameter estimation for prostate T relaxometry using convolutional neural networks.

Journal: Magma (New York, N.Y.)
PMID: 39042205

Abstract

OBJECTIVE: Quantitative parameter mapping conventionally relies on curve fitting techniques to estimate parameters from magnetic resonance image series. This study compares conventional curve fitting techniques to methods using neural networks (NN) for measuring T in the prostate.

Authors

  • Patrick J Bolan
    Center for Magnetic Resonance Research, University of Minnesota, 2021 6th Street SE, Minneapolis, MN, 55455, USA. bolan@umn.edu.
  • Sara L Saunders
    Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA.
  • Kendrick Kay
    Center for Magnetic Resonance Research(CMRR), Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA.
  • Mitchell Gross
    Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA.
  • Mehmet Akçakaya
    Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota.
  • Gregory J Metzger
    Center for Magnetic Resonance Research, University of Minnesota, 2021 6th Street SE, Minneapolis, MN, 55455, USA.

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