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Abstract #0358

A group-wise cardiac motion estimation network leveraging temporal correlation

Jiazhen Pan1, Daniel Rueckert1,2, Thomas Kuestner3, and Kerstin Hammernik1,2
1AI in Medicine, Technical University of Munich, Munich, Germany, 2Department of Computing, Imperial College London, London, United Kingdom, 3Medical Image And Data Analysis (MIDAS.lab), University Hospital of Tübingen, Tübingen, Germany


Cardiac motion estimation is the gold-standard for assessing cardiac function and complementing cardiac image reconstruction. However, previous approaches in this area either suffered from long registration times or low accuracy because the inherent temporal correlation of the cardiac motion is not leveraged. In this work, we propose a method called GRAFT, which takes multiple cardiac frames as inputs to leverage the temporal correlation. Furthermore, temporal coherence is ensured by introducing the temporal smoothness loss during the training. Our experiments indicate that GRAFT can provide competitive deformation estimation results to state-of-the-art methods and outperform them in subsequent motion-compensated MRI reconstruction.

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