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Abstract #2175

Fully automated detection of the quiescent phases of the cardiac cycle from CINE images using deep learning

Naledi Adam1, James Clough1, Ronald Mooiweer1, Phuoc Dong1, Li Huang1, Reza Razavi1, Kuberan Pushparajah1, Amedeo Chiribiri1, Andrew King1, and S├ębastien Roujol1
1Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom

Cardiac magnetic resonance (CMR) imaging is commonly performed using ECG-triggering to restrain acquisition to a quiescent phase of the cardiac cycle (i.e. end-systole or mid-diastole). Identification of the rest periods is commonly performed by visual inspection on CINE images and is thus operator dependent, time consuming and requires a trained operator. In this study, a fully automated method was developed to detect the quiescent cardiac periods of the cardiac cycle from CINE images using an integrated convolutional neural network (CNN) and long short-term memory (CNN-LSTM) network.

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