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.

中文翻译：

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心脏磁共振图像的多参数评估以区分心肌梗塞：基于张量的放射组学特征

目的：本研究使用多参数心脏磁共振成像 (CMRI) 的新型融合方法（多风味或基于张量）在四个序列上评估心肌梗死 (MI)；轴向平面中的 T1 加权 (T1W)、轴向平面中的传感平衡涡轮场回波 (sBTFE)、矢状平面中的心脏短轴晚期钆增强 (LGE-SA) 以及 LGE 的四腔视图 ( LGE-4CH) 位于轴向平面。方法：考虑纳入和排除标准后，115 名患者（83 名 MI 诊断患者和 32 名健康对照患者）纳入本研究。从整个左心室心肌（LVM）中提取放射组学特征。特征选择方法包括最小绝对收缩和选择算子 (Lasso)、最小冗余最大相关性 (MRMR)、卡方 (Chi2)、方差分析 (Anova)、递归特征消除 (RFE) 和 SelectPersentile。分类方法有支持向量机（SVM）、逻辑回归（LR）和随机森林（RF）。使用分层五重交叉验证从 CMR 图像中提取的放射组学特征计算不同的指标，包括受试者工作特征曲线 (AUC)、准确性、F1 评分、精密度、灵敏度和特异性。结果：对于 MI 检测，sBTFE 序列中的 Lasso（作为特征选择）和 RF/LR（作为分类器）具有最佳性能（AUC：0.97）。采用加权方法（作为融合图像）的 T1 + sBTFE 序列的所有特征和分类器均具有良好的性能（AUC：0.97）。此外，评估指标的结果，特别是所有模型的平均 AUC 和准确性，确定 T1 + sBTFE 加权融合方法具有很强的预测性能（AUC：0.93±0.05；准确性：0.93±0.04），其次是 T1 + sBTFE-PCA 融合方法（AUC：0.85±0.06；准确度：0.84±0.06）。结论：我们选择的 CMRI 序列表明放射组学分析能够准确检测 MI。在所研究的序列中，具有最高 AUC 和准确度值的 T1 + sBTFE 加权融合方法被选为 MI 检测的最佳技术。