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Identifying myocardial infarction using hierarchical template matching–based myocardial strain : algorithm development and usability study
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Bhalodiya, Jayendra Maganbhai, Palit, Arnab, Giblin, Gerard, Tiwari, Manoj Kumar, Prasad, Sanjay K., Bhudia, Sunil K., Arvanitis, Theodoros N. and Williams, M. A. (2021) Identifying myocardial infarction using hierarchical template matching–based myocardial strain : algorithm development and usability study. JMIR Medical Informatics, 9 (2). e22164. doi:10.2196/22164 ISSN 2291-9694.
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WRAP-identifying-myocardial-infarction-using-hierarchical-template-matching–based-myocardial-strain-Williams-2021.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1544Kb) | Preview |
Official URL: http://dx.doi.org/10.2196/22164
Abstract
Background:
Myocardial infarction (MI; location and extent of infarction) can be determined by late enhancement cardiac magnetic resonance (CMR) imaging, which requires the injection of a potentially harmful gadolinium-based contrast agent (GBCA). Alternatively, emerging research in the area of myocardial strain has shown potential to identify MI using strain values.
Objective:
This study aims to identify the location of MI by developing an applied algorithmic method of circumferential strain (CS) values, which are derived through a novel hierarchical template matching (HTM) method.
Methods:
HTM-based CS H-spread from end-diastole to end-systole was used to develop an applied method. Grid-tagging magnetic resonance imaging was used to calculate strain values in the left ventricular (LV) myocardium, followed by the 16-segment American Heart Association model. The data set was used with k-fold cross-validation to estimate the percentage reduction of H-spread among infarcted and noninfarcted LV segments. A total of 43 participants (38 MI and 5 healthy) who underwent CMR imaging were retrospectively selected. Infarcted segments detected by using this method were validated by comparison with late enhancement CMR, and the diagnostic performance of the applied algorithmic method was evaluated with a receiver operating characteristic curve test.
Results:
The H-spread of the CS was reduced in infarcted segments compared with noninfarcted segments of the LV. The reductions were 30% in basal segments, 30% in midventricular segments, and 20% in apical LV segments. The diagnostic accuracy of detection, using the reported method, was represented by area under the curve values, which were 0.85, 0.82, and 0.87 for basal, midventricular, and apical slices, respectively, demonstrating good agreement with the late-gadolinium enhancement–based detections.
Conclusions:
The proposed applied algorithmic method has the potential to accurately identify the location of infarcted LV segments without the administration of late-gadolinium enhancement. Such an approach adds the potential to safely identify MI, potentially reduce patient scanning time, and extend the utility of CMR in patients who are contraindicated for the use of GBCA.
| Item Type: | Journal Article | ||||||||||||||||||||||||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software R Medicine > RC Internal medicine |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||||||||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | Myocardial infarction, Cardiovascular system -- Magnetic resonance imaging, Heart -- Left ventricle | ||||||||||||||||||||||||||||||
| Journal or Publication Title: | JMIR Medical Informatics | ||||||||||||||||||||||||||||||
| Publisher: | JMIR Publications | ||||||||||||||||||||||||||||||
| ISSN: | 2291-9694 | ||||||||||||||||||||||||||||||
| Official Date: | 10 February 2021 | ||||||||||||||||||||||||||||||
| Dates: |
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| Volume: | 9 | ||||||||||||||||||||||||||||||
| Number: | 2 | ||||||||||||||||||||||||||||||
| Article Number: | e22164 | ||||||||||||||||||||||||||||||
| DOI: | 10.2196/22164 | ||||||||||||||||||||||||||||||
| Status: | Peer Reviewed | ||||||||||||||||||||||||||||||
| Publication Status: | Published | ||||||||||||||||||||||||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||||||||||||||||||||
| Date of first compliant deposit: | 11 February 2021 | ||||||||||||||||||||||||||||||
| Date of first compliant Open Access: | 12 February 2021 | ||||||||||||||||||||||||||||||
| Funder: | Health Data Research (HDR) UK | ||||||||||||||||||||||||||||||
| RIOXX Funder/Project Grant: |
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| Is Part Of: | 1 | ||||||||||||||||||||||||||||||
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