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Computational and Mathematical Methods in Medicine
Volume 2017, Article ID 1640835, 10 pages
Research Article

Convolutional Neural Network for the Detection of End-Diastole and End-Systole Frames in Free-Breathing Cardiac Magnetic Resonance Imaging

1School of Biology & Engineering, Guizhou Medical University, Guiyang, Guizhou Province, China
2State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu Province, China
3Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou Province, China

Correspondence should be addressed to Yan He; moc.621@4040nayeh

Received 13 March 2017; Revised 18 May 2017; Accepted 20 June 2017; Published 26 July 2017

Academic Editor: Michele Migliore

Copyright © 2017 Fan Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Free-breathing cardiac magnetic resonance (CMR) imaging has short examination time with high reproducibility. Detection of the end-diastole and the end-systole frames of the free-breathing cardiac magnetic resonance, supplemented by visual identification, is time consuming and laborious. We propose a novel method for automatic identification of both the end-diastole and the end-systole frames, in the free-breathing CMR imaging. The proposed technique utilizes the convolutional neural network to locate the left ventricle and to obtain the end-diastole and the end-systole frames from the respiratory motion signal. The proposed procedure works successfully on our free-breathing CMR data, and the results demonstrate a high degree of accuracy and stability. Convolutional neural network improves the postprocessing efficiency greatly and facilitates the clinical application of the free-breathing CMR imaging.