Abstract #2875

Deep Learning Algorithms May Aid In The Evaluation Of Cine Images In Patients With Atrial Arrhythmia – A Case Series

Ria Garg^1,2, Elizabeth Hillier^2,3,4, Andrew Coristine^2,3,5, and Matthias G. Friedrich^2,3

¹Cardiovascular imaging, RI-McGill University Health Center, Montreal, QC, Canada, ²Faculty of Medicine, McGill University Health Center, Montreal, QC, Canada, ³RI-McGill University Health Center, Montreal, QC, Canada, ⁴Faculty of Medicine, University of Alberta, Edmonton, Alberta, AB, Canada, ⁵MR applications and workflow, GE Healthcare, Montreal, QC, Canada

Cardiovascular magnetic resonance cine imaging is utilised to give comprehensive information on ventricular function. Arrhythmia obstructs acquisition of these images, where the use of faster acquisition protocols with deep learning reconstruction methods may aid in solving the problem. We evaluated cine images of three patients in atrial arrhythmia, acquired using standard method; fast, variable density spatiotemporal sampling acquisition (VD kt) of one(1rr) or three(3rr) heart beats; and deep learning reconstruction of the same (DL-1rr,DL-3rr). Our results showed that undersampled techniques combined with deep-learning algorithms result in image quality improvements with no significant difference in quantitative values between all acquisition techniques.

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