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Abstract #0116

Self-supervised motion-compensated reconstruction for cardiac Cine MRI

Siying Xu1, Aya Ghoul1, Kerstin Hammernik2, Jens Kuebler3, Patrick Krumm3, Andreas Lingg3, Daniel Rueckert2,4,5, Sergios Gatidis1,6, and Thomas Kuestner1
1Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University of Tuebingen, Tübingen, Germany, 2School of Computation, Information and Technology, Technical University of Munich, Munich, Germany, 3Department of Diagnostic and Interventional Radiology, University of Tuebingen, Tübingen, Germany, 4Department of Computing, Imperial College London, London, United Kingdom, 5Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany, 6Department of Radiology, Stanford University, Stanford, CA, United States

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, Motion-compensated reconstruction, Self-supervised learning

Motivation: To enable single breath-hold cardiac Cine MRI with efficient deep learning reconstruction that utilizes only undersampled training data. Current existing self-supervised approaches mainly focus on static images.

Goal(s): We aim to develop a self-supervised method for dynamic MRI reconstruction without needing fully-sampled data while striving to achieve high-quality reconstruction under high acceleration rates.

Approach: We propose SSL-MoCo, which combines self-supervised learning with motion-compensated reconstruction for a joint image registration and reconstruction network.

Results: SSL-MoCo achieves high-quality reconstruction performance and improved artifact removal compared to other self-supervised methods in highly accelerated cardiac Cine MRI, even outperforming supervised learning while not requiring fully-sampled data.

Impact: We propose a fully self-supervised framework that enables high-quality reconstruction of cardiac Cine MRI acquired in a single breath-hold. The adaptability of this framework opens new research avenues for leveraging undersampled data and extends to other dynamic MRI modalities.

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