1. Siamese pyramidal deep learning network for strain estimation in 3D cardiac cine-MR.
- Author
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V Graves C, Rebelo MFS, Moreno RA, Dantas-Jr RN, Assunção-Jr AN, Nomura CH, and Gutierrez MA
- Subjects
- Reproducibility of Results, Magnetic Resonance Imaging, Cine methods, Heart diagnostic imaging, Heart Ventricles diagnostic imaging, Deep Learning
- Abstract
Strain represents the quantification of regional tissue deformation within a given area. Myocardial strain has demonstrated considerable utility as an indicator for the assessment of cardiac function. Notably, it exhibits greater sensitivity in detecting subtle myocardial abnormalities compared to conventional cardiac function indices, like left ventricle ejection fraction (LVEF). Nonetheless, the estimation of strain poses considerable challenges due to the necessity for precise tracking of myocardial motion throughout the complete cardiac cycle. This study introduces a novel deep learning-based pipeline, designed to automatically and accurately estimate myocardial strain from three-dimensional (3D) cine-MR images. Consequently, our investigation presents a comprehensive pipeline for the precise quantification of local and global myocardial strain. This pipeline incorporates a supervised Convolutional Neural Network (CNN) for accurate segmentation of the cardiac muscle and an unsupervised CNN for robust left ventricle motion tracking, enabling the estimation of strain in both artificial phantoms and real cine-MR images. Our investigation involved a comprehensive comparison of our findings with those obtained from two commonly utilized commercial software in this field. This analysis encompassed the examination of both intra- and inter-user variability. The proposed pipeline exhibited demonstrable reliability and reduced divergence levels when compared to alternative systems. Additionally, our approach is entirely independent of previous user data, effectively eliminating any potential user bias that could influence the strain analyses., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Catharine De Vita Graves reports financial support was provided by Canon Medical Systems Corporation. Marina F. S. Rebelo reports financial support was provided by Canon Medical Systems Corporation. Ramon A. Moreno reports financial support was provided by Canon Medical Systems Corporation. Cesar H. Nomura reports financial support was provided by Canon Medical Systems Corporation. Marco A. Gutierrez reports financial support was provided by Canon Medical Systems Corporation. Catharine De Vita Graves has patent #BR512022001946-9 licensed to INPI – Instituto Nacional da Propriedade Industrial. Marina F. S. Rebelo has patent #BR512022001946-9 licensed to INPI – Instituto Nacional da Propriedade Industrial. Ramon A. Moreno has patent #BR512022001946-9 licensed to INPI – Instituto Nacional da Propriedade Industrial. Cesar H. Nomura has patent #BR512022001946-9 licensed to INPI - Instituto Nacional da Propriedade Industrial. Marco A. Gutierrez has patent #BR512022001946-9 licensed to INPI – Instituto Nacional da Propriedade Industrial., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)
- Published
- 2023
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