Fully automated myocardial strain estimation from cine MRI using convolutional neural networks

Puyol-Anton E, Ruijsink B, Bai W, Langet H, De Craene M, Schnabel JA, Piro P, King AP, Sinclair M (2018)

Publication Type: Conference contribution

Publication year: 2018

Journal

Proceedings - International Symposium on Biomedical Imaging

Publisher: IEEE Computer Society

Book Volume: 2018-April

Pages Range: 1139-1143

Conference Proceedings Title: Proceedings - International Symposium on Biomedical Imaging

Event location: Washington, DC, USA

ISBN: 9781538636367

DOI: 10.1109/ISBI.2018.8363772

Abstract

Cardiovascular magnetic resonance myocardial feature tracking (CMR-FT) is a promising method for quantification of cardiac function from standard steady-state free precession (SSFP) images. However, currently available techniques require operator dependent and time-consuming manual intervention, limiting reproducibility and clinical use. In this paper, we propose a fully automated pipeline to compute left ventricular (LV) longitudinal and radial strain from 2- and 4-chamber cine acquisitions, and LV circumferential and radial strain from the short-axis imaging. The method employs a convolutional neural network to automatically segment the myocardium, followed by feature tracking and strain estimation. Experiments are performed using 40 healthy volunteers and 40 ischemic patients from the UK Biobank dataset. Results show that our method obtained strain values that were in excellent agreement with the commercially available clinical CMR-FT software CVI42 (Circle Cardiovascular Imaging, Calgary, Canada).

Involved external institutions

King’s College London GB United Kingdom (GB) Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) Philips Electronics Nederland BV NL Netherlands (NL)

How to cite

APA:

Puyol-Anton, E., Ruijsink, B., Bai, W., Langet, H., De Craene, M., Schnabel, J.A.,... Sinclair, M. (2018). Fully automated myocardial strain estimation from cine MRI using convolutional neural networks. In Proceedings - International Symposium on Biomedical Imaging (pp. 1139-1143). Washington, DC, USA: IEEE Computer Society.

MLA:

Puyol-Anton, Esther, et al. "Fully automated myocardial strain estimation from cine MRI using convolutional neural networks." Proceedings of the 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018, Washington, DC, USA IEEE Computer Society, 2018. 1139-1143.

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