Multi-parametric assessment of cardiac magnetic resonance images to distinguish myocardial infarctions: A tensor-based radiomics feature

Article type: Research Article

Authors: Wang, Dehua^a | Jasim Taher, Hayder^{b; *} | Al-Fatlawi, Murtadha^{c; d} | Abdullah, Badr Ahmed^e | Khayatovna Ismailova, Munojat^f | Abedi-Firouzjah, Razzagh^g

Affiliations: [a] Department of Imaging, The First People’s Hospital of Lianyungang, Lianyungang City, Jiangsu Province, China | [b] Department of Radiology, Hilla University College, Babylon, Iraq | [c] Department of Radiological Techniques, College of Health and Medical Techniques, Al-Mustaqbal University, Babylon, Iraq | [d] Shaheed Al-Muhrab Center of Cath & Cardiac Surgery’s, Babil Health Directorate, Babylon, Iraq | [e] Institute of Radiology, City of Medicine Directorate, Baghdad, Iraq | [f] Department of Medical Radiology, Tashkent Medical Academy, Tashkent, Uzbekistan | [g] Department of Medical Physics Radiobiology and Radiation Protection, School of Medicine, Babol University of Medical Sciences, Babol, Iran

Correspondence: [*] Corresponding authors: Hayder Jasim Taher, Department of Radiology, Hilla University College, Babylon, Iraq. E-mail: [email protected].

Abstract: AIM:This study assessed the myocardial infarction (MI) using a novel fusion approach (multi-flavored or tensor-based) of multi-parametric cardiac magnetic resonance imaging (CMRI) at four sequences; T1-weighted (T1W) in the axial plane, sense-balanced turbo field echo (sBTFE) in the axial plane, late gadolinium enhancement of heart short axis (LGE-SA) in the sagittal plane, and four-chamber views of LGE (LGE-4CH) in the axial plane. METHODS:After considering the inclusion and exclusion criteria, 115 patients (83 with MI diagnosis and 32 as healthy control patients), were included in the present study. Radiomic features were extracted from the whole left ventricular myocardium (LVM). Feature selection methods were Least Absolute Shrinkage and Selection Operator (Lasso), Minimum Redundancy Maximum Relevance (MRMR), Chi-Square (Chi2), Analysis of Variance (Anova), Recursive Feature Elimination (RFE), and SelectPersentile. The classification methods were Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF). Different metrics, including receiver operating characteristic curve (AUC), accuracy, F1- score, precision, sensitivity, and specificity were calculated for radiomic features extracted from CMR images using stratified five-fold cross-validation. RESULTS:For the MI detection, Lasso (as the feature selection) and RF/LR (as the classifiers) in sBTFE sequences had the best performance (AUC: 0.97). All features and classifiers of T1 + sBTFE sequences with the weighted method (as the fused image), had a good performance (AUC: 0.97). In addition, the results of the evaluated metrics, especially mean AUC and accuracy for all models, determined that the T1 + sBTFE-weighted fused method had strong predictive performance (AUC: 0.93±0.05; accuracy: 0.93±0.04), followed by T1 + sBTFE-PCA fused method (AUC: 0.85±0.06; accuracy: 0.84±0.06). CONCLUSION:Our selected CMRI sequences demonstrated that radiomics analysis enables to detection of MI accurately. Among the investigated sequences, the T1 + sBTFE-weighted fused method with the highest AUC and accuracy values was chosen as the best technique for MI detection.

Keywords: Myocardial infarction, cardiac magnetic resonance images, multi-parametric, tensor-based, radiomics feature, machine learning

DOI: 10.3233/XST-230307

Journal: Journal of X-Ray Science and Technology, vol. Pre-press, no. Pre-press, pp. 1-15, 2024