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DeepStrain: A Deep Learning Workflow for the Automated Characterization of Cardiac Mechanics

Manuel A. Morales, Maaike van den Boomen, Christopher Nguyen, View ORCID ProfileJayashree Kalpathy-Cramer, Bruce R. Rosen, Collin M. Stultz, David Izquierdo-Garcia, View ORCID ProfileCiprian Catana
doi: https://doi.org/10.1101/2021.01.05.425266
Manuel A. Morales
1Center for Biomedical Imaging, MGH, HMS, 149 13th St, Boston, MA 02129
2Harvard-MIT Health Science and Technology, 77 Massachusetts Ave, Cambridge, MA, 02139.
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Maaike van den Boomen
3Cardiovascular Research Center and Martinos Center for Biomedical Imaging, MGH, HMS, 149 13th St, Boston, MA 02129
4Department of Radiology
5University Medical Center Groningen, 9713 GZ Groningen
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Christopher Nguyen
3Cardiovascular Research Center and Martinos Center for Biomedical Imaging, MGH, HMS, 149 13th St, Boston, MA 02129
4Department of Radiology
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Jayashree Kalpathy-Cramer
1Center for Biomedical Imaging, MGH, HMS, 149 13th St, Boston, MA 02129
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  • ORCID record for Jayashree Kalpathy-Cramer
Bruce R. Rosen
1Center for Biomedical Imaging, MGH, HMS, 149 13th St, Boston, MA 02129
2Harvard-MIT Health Science and Technology, 77 Massachusetts Ave, Cambridge, MA, 02139.
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Collin M. Stultz
2Harvard-MIT Health Science and Technology, 77 Massachusetts Ave, Cambridge, MA, 02139.
6Electrical Engineering and Computer Science
7Division of Cardiology, MGH, 55 Fruit St, Boston, MA, 02114
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David Izquierdo-Garcia
1Center for Biomedical Imaging, MGH, HMS, 149 13th St, Boston, MA 02129
2Harvard-MIT Health Science and Technology, 77 Massachusetts Ave, Cambridge, MA, 02139.
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  • For correspondence: davidizq@nmr.mgh.harvard.edu ccatana@mgh.harvard.edu
Ciprian Catana
1Center for Biomedical Imaging, MGH, HMS, 149 13th St, Boston, MA 02129
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  • ORCID record for Ciprian Catana
  • For correspondence: davidizq@nmr.mgh.harvard.edu ccatana@mgh.harvard.edu
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Abstract

Myocardial strain analysis from cinematic magnetic resonance imaging (cine-MRI) data could provide a more thorough characterization of cardiac mechanics than volumetric parameters such as left-ventricular ejection fraction, but sources of variation including segmentation and motion estimation have limited its wide clinical use. We designed and validated a deep learning (DL) workflow to generate both volumetric parameters and strain measures from cine-MRI data, including strain rate (SR) and regional strain polar maps, consisting of segmentation and motion estimation convolutional neural networks developed and trained using healthy and cardiovascular disease (CVD) subjects (n=150). DL-based volumetric parameters were correlated (>0.98) and without significant bias relative to parameters derived from manual segmentations in 50 healthy and CVD subjects. Compared to landmarks manually-tracked on tagging-MRI images from 15 healthy subjects, landmark deformation using DL-based motion estimates from paired cine-MRI data resulted in an end-point-error of 2.9 ± 1.5 mm. Measures of end-systolic global strain from these cine-MRI data showed no significant biases relative to a tagging-MRI reference method. On 4 healthy subjects, intraclass correlation coefficient for intra-scanner repeatability was excellent (>0.95) for strain, moderate to excellent for SR (0.690-0.963), and good to excellent (0.826-0.994) in most polar map segments. Absolute relative change was within ~5% for strain, within ~10% for SR, and <1% in half of polar map segments. In conclusion, we developed and evaluated a DL-based, end-to-end fully-automatic workflow for global and regional myocardial strain analysis to quantitatively characterize cardiac mechanics of healthy and CVD subjects based on ubiquitously acquired cine-MRI data.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Submitted for review on Dec 20, 2020. This work was supported in part by the U.S. National Cancer Institute under Grant 1R01CA218187-01A1.

  • (email: moralesq{at}mit.edu; brrosen{at}mgh.harvard.edu), (email: mvandenboomen{at}mgh.harvard.edu; Christopher.nguyen{at}mgh.havard.edu), (cmstultz{at}mit.edu), (jkalpathy-cramer{at}mgh.harvard.edu).

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 07, 2021.
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DeepStrain: A Deep Learning Workflow for the Automated Characterization of Cardiac Mechanics
Manuel A. Morales, Maaike van den Boomen, Christopher Nguyen, Jayashree Kalpathy-Cramer, Bruce R. Rosen, Collin M. Stultz, David Izquierdo-Garcia, Ciprian Catana
bioRxiv 2021.01.05.425266; doi: https://doi.org/10.1101/2021.01.05.425266
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DeepStrain: A Deep Learning Workflow for the Automated Characterization of Cardiac Mechanics
Manuel A. Morales, Maaike van den Boomen, Christopher Nguyen, Jayashree Kalpathy-Cramer, Bruce R. Rosen, Collin M. Stultz, David Izquierdo-Garcia, Ciprian Catana
bioRxiv 2021.01.05.425266; doi: https://doi.org/10.1101/2021.01.05.425266

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