Rapid 2D Na MRI of the calf using a denoising convolutional neural network.

Journal: Magnetic resonance imaging
PMID: 38642779

Abstract

PURPOSE: Na MRI can be used to quantify in-vivo tissue sodium concentration (TSC), but the inherently low Na signal leads to long scan times and/or noisy or low-resolution images. Reconstruction algorithms such as compressed sensing (CS) have been proposed to mitigate low signal-to-noise ratio (SNR); although, these can result in unnatural images, suboptimal denoising and long processing times. Recently, machine learning has been increasingly used to denoise H MRI acquisitions; however, this approach typically requires large volumes of high-quality training data, which is not readily available for Na MRI. Here, we propose using H data to train a denoising convolutional neural network (CNN), which we subsequently demonstrate on prospective Na images of the calf.

Authors

  • Rebecca R Baker
    UCL Centre for Medical Imaging, University College London, London, UK; UCL Centre for Translational Cardiovascular Imaging, University College London, London, UK. Electronic address: r.baker.17@ucl.ac.uk.
  • Vivek Muthurangu
    UCL Centre for Cardiovascular Imaging, University College London, London, United Kingdom.
  • Marilena Rega
    Institute of Nuclear Medicine, University College Hospital, London, UK. Electronic address: marilena.rega@nhs.net.
  • Stephen B Walsh
    Department of Renal Medicine, University College London, London, UK. Electronic address: stephen.walsh@ucl.ac.uk.
  • Jennifer A Steeden
    UCL Centre for Cardiovascular Imaging, University College London, London, United Kingdom.

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