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Abstract #3528

RAIDER-SSL: Self-supervised machine learning for rapid, anatomy-independent PDFF and R2* estimation using magnitude-only signals

Giulio V Minore1,2, Louis Dwyer-Hemmings1,3,4, Timothy JP Bray1,3,4, and Hui Zhang1,5
1Hawkes Institute, University College London, London, United Kingdom, 2Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, 3Centre for Medical Imaging, University College London, London, United Kingdom, 4Department of Imaging, University College London Hospital, London, United Kingdom, 5Department of Computer Science, University College London, London, United Kingdom

Synopsis

Keywords: Analysis/Processing, Fat and Fat/Water Separation

Motivation: RAIDER[1] enables voxel-wise, rapid, anatomy-independent PDFF and R2* estimation using supervised learning. However, supervised learning requires target data distribution known a priori; its performance may suffer if out-of distribution data is encountered. Self-supervised learning (SSL) has been proposed to address this limitation.

Goal(s): To develop a self-supervised alternative to RAIDER.

Approach: The dual network approach of RAIDER is adapted to SSL. This is compared against the standard SSL which uses a single network.

Results: RAIDER-SSL outperforms the standard SSL implementation, both in simulation and in vivo.

Impact: RAIDER-SSL enables PDFF and R2* estimation from voxel-wise, magnitude-only CSE-MRI data while avoiding the potential performance loss associatedwith distributional shift that may appear with supervised learning methods.

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Keywords