Hierarchical Template Matching for 3D Myocardial Tracking and Cardiac Strain Estimation

Myocardial tracking and strain estimation can non-invasively assess cardiac functioning using subject-specific MRI. As the left-ventricle does not have a uniform shape and functioning from base to apex, the development of 3D MRI has provided opportunities for simultaneous 3D tracking, and 3D strain...

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Bibliographic Details
Published in:Scientific reports 2019-08, Vol.9 (1), p.12450-13, Article 12450
Main Authors: Bhalodiya, Jayendra M., Palit, Arnab, Ferrante, Enzo, Tiwari, Manoj K., Bhudia, Sunil K., Arvanitis, Theodoros N., Williams, Mark A.
Format: Article
Language:English
Subjects:
639/166/985
639/705/117
Accuracy
Algorithms
Datasets
Heart
Humanities and Social Sciences
Humans
Image processing
Imaging, Three-Dimensional
Magnetic Resonance Imaging
Methods
Models, Cardiovascular
multidisciplinary
Myocardium
Science
Science (multidisciplinary)
Spatial analysis
Three dimensional imaging
Ultrasonic imaging
Ventricle
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Summary:Myocardial tracking and strain estimation can non-invasively assess cardiac functioning using subject-specific MRI. As the left-ventricle does not have a uniform shape and functioning from base to apex, the development of 3D MRI has provided opportunities for simultaneous 3D tracking, and 3D strain estimation. We have extended a Local Weighted Mean (LWM) transformation function for 3D, and incorporated in a Hierarchical Template Matching model to solve 3D myocardial tracking and strain estimation problem. The LWM does not need to solve a large system of equations, provides smooth displacement of myocardial points, and adapt local geometric differences in images. Hence, 3D myocardial tracking can be performed with 1.49 mm median error, and without large error outliers. The maximum error of tracking is up to 24% reduced compared to benchmark methods. Moreover, the estimated strain can be insightful to improve 3D imaging protocols, and the computer code of LWM could also be useful for geo-spatial and manufacturing image analysis researchers.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-019-48927-2