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Fully automated myocardial strain estimation from cine MRI using convolutional neural networks.
In: (2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 04-07 April 2018, Washington, DC, USA). 2018. 1139-1143 (Proceedings - International Symposium on Biomedical Imaging ; 2018-April)
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).
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Publikationstyp
Artikel: Konferenzbeitrag
Schlagwörter
Automatic Pipeline ; Machine Learning ; Mri ; Myocardial Strain
ISSN (print) / ISBN
1945-7928
e-ISSN
1945-8452
Konferenztitel
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
Konferzenzdatum
04-07 April 2018
Konferenzort
Washington, DC, USA
Quellenangaben
Band: 2018-April,
Seiten: 1139-1143
Nichtpatentliteratur
Publikationen
Institut(e)
Institute for Machine Learning in Biomed Imaging (IML)