Abstract #1754

Deep artifact suppression for interventional cardiac MR-thermometry

Olivier Jaubert¹, Valery Ozenne^2,3,4,5, Maxime Yon^2,3,4, Javier Montalt-Tordera¹, Jennifer Steeden¹, Vivek Muthurangu¹, and Bruno Quesson^2,3,4

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

Synopsis

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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