Parametric Methods for the Regional Assessment of Cardiac Wall Motion Abnormalities: Comparison Study
- Narjes Benameur1,*, Mazin Abed Mohammed2, Ramzi Mahmoudi3,4, Younes Arous5, Begonya Garcia-Zapirain6, Karrar Hameed Abdulkareem7, Mohamed Hedi Bedoui3
1 University of Tunis El Manar, Higher Institute of Medical Technologies of Tunis, Laboratory of Biophysics and Medical Technologies, Tunis, Tunisia
2 College of Computer Science and Information Technology, University of Anbar, 31001, Anbar, Iraq
3 Faculty of Medicine of Monastir, Medical Imaging Technology Lab - LTIM-LR12ES06, University of Monastir, 5019, Monastir, Tunisia
4 Gaspard-Monge Computer-Science Laboratory, Paris-Est University, Mixed Unit CNRS-UMLV-ESIEE UMR8049, BP99, ESIEE Paris Cité Descartes, 93162 Noisy Le Grand, France
5 Military Hospital of Instruction of Tunis, Tunis, Tunisia
6 eVIDA Lab, University of Deusto, Avda/Universidades 24, Bilbao, 48007, Spain
7 College of Agriculture, Al-Muthanna University, Samawah, 66001, Iraq
* Corresponding Author: Narjes Benameur. Email:
(This article belongs to this Special Issue: Advanced signal acquisition and processing for Internet of Medical Things)
2 College of Computer Science and Information Technology, University of Anbar, 31001, Anbar, Iraq
3 Faculty of Medicine of Monastir, Medical Imaging Technology Lab - LTIM-LR12ES06, University of Monastir, 5019, Monastir, Tunisia
4 Gaspard-Monge Computer-Science Laboratory, Paris-Est University, Mixed Unit CNRS-UMLV-ESIEE UMR8049, BP99, ESIEE Paris Cité Descartes, 93162 Noisy Le Grand, France
5 Military Hospital of Instruction of Tunis, Tunis, Tunisia
6 eVIDA Lab, University of Deusto, Avda/Universidades 24, Bilbao, 48007, Spain
7 College of Agriculture, Al-Muthanna University, Samawah, 66001, Iraq
* Corresponding Author: Narjes Benameur. Email:
(This article belongs to this Special Issue: Advanced signal acquisition and processing for Internet of Medical Things)
Received 14 January 2021; Accepted 25 March 2021; Issue published 04 June 2021
Abstract
Left ventricular (LV) dysfunction is mainly assessed by global contractile indices such as ejection fraction and LV Volumes in cardiac MRI. While these indices give information about the presence or not of LV alteration, they are not able to identify the location and the size of such alteration. The aim of this study is to compare the performance of three parametric imaging techniques used in cardiac MRI for the regional quantification of cardiac dysfunction. The proposed approaches were evaluated on 20 patients with myocardial infarction and 20 subjects with normal function. Three parametric images approaches: covariance analysis, parametric images based on Hilbert transform and those based on the monogenic signal were evaluated using cine-MRI frames acquired in three planes of views. The results show that parametric images generated from the monogenic signal were superior in term of sensitivity (89.69%), specificity (86.51%) and accuracy (89.06%) to those based on covariance analysis and Hilbert transform in the detection of contractile dysfunction related to myocardial infarction. Therefore, the parametric image based on the monogenic signal is likely to provide additional regional indices about LV dysfunction and it may be used in clinical practice as a tool for the analysis of the myocardial alterations.
Keywords
Covariance analysis; cardiac MRI; monogenic signal; assessment; Hilbert transform
Cite This Article
N. Benameur, M. A. Mohammed, R. Mahmoudi, Y. Arous, B. Garcia-Zapirain et al., "Parametric methods for the regional assessment of cardiac wall motion abnormalities: comparison study," Computers, Materials & Continua, vol. 69, no.1, pp. 1233–1252, 2021.
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