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Abstract #1754

Deep artifact suppression for interventional cardiac MR-thermometry

Olivier Jaubert1, Valery Ozenne2,3,4,5, Maxime Yon2,3,4, Javier Montalt-Tordera1, Jennifer Steeden1, Vivek Muthurangu1, and Bruno Quesson2,3,4
1Institute of Cardiovascular Science, University College London, London, United Kingdom, 2Electrophysiology and Heart Modeling Institute, Foundation Bordeaux Université, IHU Lyric, Bordeaux, France, 3Centre de recherche Cardio-Thoracique de Bordeaux, Universite Bordeaux, Bordeaux, France, 4Centre de recherche Cardio-Thoracique de Bordeaux, INSERM, Bordeaux, France, 5Centre de Résonance Magnétique des Systèmes Biologiques, Bordeaux, France


A novel thermometry acquisition and a fast deep learning based image reconstruction were combined for cardiac interventional thermometry at high spatial (0.86×0.86mm2) and temporal (0.97s) resolutions, robust to motion and susceptibility artefact and independent of external ECG-gating. The method was tested in phantom and in-vivo in a sheep. The proposed deep learning method outperformed the state-of-the-art algorithm in terms of SNR and paves the way for clinical studies.

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