Abstract #4243

Toward automatic lesion transmurality assessment using machine learning: a proof of concept in preclinical EP studies under MRI-guidance

Valéry Ozenne^1,2,3,4, Pierre Bour^2,3,4, Marylène Delcey^2,3,4, Nicolas Cedilnik⁵, Maxime Sermesant⁵, and Bruno Quesson^2,3,4

¹Centre de Résonance Magnétique des Systèmes Biologiques, UMR 5536, CNRS, Bordeaux, France, ²IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Bordeaux, France, ³Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, U1045, Bordeaux, France, ⁴INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, U1045, Bordeaux, France, ⁵Université Côte d’Azur, Inria, Epione, Sophia Antipolis, France

MR-guidance of electrophysiological (EP) procedures requires manual segmentation of the cardiac cavities either at the beginning of the procedure to produce the roadmap volume or after radiofrequency ablation (RFA) to assess the lesion transmurality in post ablation images. The purpose of this work is to evaluate the feasibility of automatic in-line segmentation in the context of routine preclinical EP studies.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.