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

Esther Puyol-Anton; Bram Ruijsink; Wenjia Bai; Helene Langet; Mathieu De Craene; Julia A. Schnabel; Paolo Piro; Andrew P. King; Matthew Sinclair

doi:10.1109/ISBI.2018.8363772

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

Esther Puyol-Anton, Bram Ruijsink, Wenjia Bai, Helene Langet, Mathieu De Craene, Julia A. Schnabel, Paolo Piro, Andrew P. King, Matthew Sinclair

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

20 Scopus citations

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).

Original language	English
Title of host publication	2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
Publisher	IEEE Computer Society
Pages	1139-1143
Number of pages	5
ISBN (Electronic)	9781538636367
DOIs	https://doi.org/10.1109/ISBI.2018.8363772
State	Published - 23 May 2018
Externally published	Yes
Event	15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States Duration: 4 Apr 2018 → 7 Apr 2018

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2018-April
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
Country/Territory	United States
City	Washington
Period	4/04/18 → 7/04/18

Keywords

Automatic pipeline
MRI
Machine learning
Myocardial Strain

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10.1109/ISBI.2018.8363772

Cite this

Puyol-Anton, E., Ruijsink, B., Bai, W., Langet, H., De Craene, M., Schnabel, J. A., Piro, P., King, A. P., & Sinclair, M. (2018). Fully automated myocardial strain estimation from cine MRI using convolutional neural networks. In 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018 (pp. 1139-1143). (Proceedings - International Symposium on Biomedical Imaging; Vol. 2018-April). IEEE Computer Society. https://doi.org/10.1109/ISBI.2018.8363772

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title = "Fully automated myocardial strain estimation from cine MRI using convolutional neural networks",

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).",

keywords = "Automatic pipeline, MRI, Machine learning, Myocardial Strain",

author = "Esther Puyol-Anton and Bram Ruijsink and Wenjia Bai and Helene Langet and {De Craene}, Mathieu and Schnabel, {Julia A.} and Paolo Piro and King, {Andrew P.} and Matthew Sinclair",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 ; Conference date: 04-04-2018 Through 07-04-2018",

year = "2018",

month = may,

day = "23",

doi = "10.1109/ISBI.2018.8363772",

language = "English",

series = "Proceedings - International Symposium on Biomedical Imaging",

publisher = "IEEE Computer Society",

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Puyol-Anton, E, Ruijsink, B, Bai, W, Langet, H, De Craene, M, Schnabel, JA, Piro, P, King, AP & Sinclair, M 2018, Fully automated myocardial strain estimation from cine MRI using convolutional neural networks. in 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. Proceedings - International Symposium on Biomedical Imaging, vol. 2018-April, IEEE Computer Society, pp. 1139-1143, 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018, Washington, United States, 4/04/18. https://doi.org/10.1109/ISBI.2018.8363772

Fully automated myocardial strain estimation from cine MRI using convolutional neural networks. / Puyol-Anton, Esther; Ruijsink, Bram; Bai, Wenjia et al.
2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. IEEE Computer Society, 2018. p. 1139-1143 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2018-April).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Puyol-Anton, Esther

AU - Ruijsink, Bram

AU - Bai, Wenjia

AU - Langet, Helene

AU - De Craene, Mathieu

AU - Schnabel, Julia A.

AU - Piro, Paolo

AU - King, Andrew P.

AU - Sinclair, Matthew

PY - 2018/5/23

Y1 - 2018/5/23

N2 - 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).

AB - 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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Puyol-Anton E, Ruijsink B, Bai W, Langet H, De Craene M, Schnabel JA et al. Fully automated myocardial strain estimation from cine MRI using convolutional neural networks. In 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. IEEE Computer Society. 2018. p. 1139-1143. (Proceedings - International Symposium on Biomedical Imaging). doi: 10.1109/ISBI.2018.8363772

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

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