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Self-supervised Learning of Inverse Problem Solvers in Medical Imaging

  • Ortal Senouf
  • Sanketh VedulaEmail author
  • Tomer Weiss
  • Alex Bronstein
  • Oleg Michailovich
  • Michael Zibulevsky
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11795)

Abstract

In the past few years, deep learning-based methods have demonstrated enormous success for solving inverse problems in medical imaging. In this work, we address the following question: Given a set of measurements obtained from real imaging experiments, what is the best way to use a learnable model and the physics of the modality to solve the inverse problem and reconstruct the latent image? Standard supervised learning based methods approach this problem by collecting data sets of known latent images and their corresponding measurements. However, these methods are often impractical due to the lack of availability of appropriately sized training sets, and, more generally, due to the inherent difficulty in measuring the “groundtruth” latent image. In light of this, we propose a self-supervised approach to training inverse models in medical imaging in the absence of aligned data. Our method only requiring access to the measurements and the forward model at training. We showcase its effectiveness on inverse problems arising in accelerated magnetic resonance imaging (MRI).

Keywords

Deep learning Inverse problems Self-supervised learning Accelerated MRI 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ortal Senouf
    • 1
  • Sanketh Vedula
    • 1
    Email author
  • Tomer Weiss
    • 1
  • Alex Bronstein
    • 1
  • Oleg Michailovich
    • 2
  • Michael Zibulevsky
    • 1
  1. 1.TechnionHaifaIsrael
  2. 2.University of WaterlooWaterlooCanada

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