T1 and T2 Mapping in Cardiology: "Mapping the Obscure Object of Desire".

The increasing use of cardiovascular magnetic resonance (CMR) is based on its capability to perform biventricular function assessment and tissue characterization without radiation and with high reproducibility. The use of late gadolinium enhancement (LGE) gave the potential of non-invasive biopsy for fibrosis quantification. However, LGE is unable to detect diffuse myocardial disease. Native T1 mapping and extracellular volume fraction (ECV) provide knowledge about pathologies affecting both the myocardium and interstitium that is otherwise difficult to identify. Changes of myocardial native T1 reflect cardiac diseases (acute coronary syndromes, infarction, myocarditis, and diffuse fibrosis, all with high T1) and systemic diseases such as cardiac amyloid (high T1), Anderson-Fabry disease (low T1), and siderosis (low T1). The ECV, an index generated by native and post-contrast T1 mapping, measures the cellular and extracellular interstitial matrix (ECM) compartments. This myocyte-ECM dichotomy has important implications for identifying specific therapeutic targets of great value for heart failure treatment. On the other hand, T2 mapping is superior compared with myocardial T1 and ECM for assessing the activity of myocarditis in recent-onset heart failure. Although these indices can significantly affect the clinical decision making, multicentre studies and a community-wide approach (including MRI vendors, funding, software, contrast agent manufacturers, and clinicians) are still missing.

Machine learning of native T1 mapping radiomics for classification of hypertrophic cardiomyopathy phenotypes.

We explored whether radiomic features from T1 maps by cardiac magnetic resonance (CMR) could enhance the diagnostic value of T1 mapping in distinguishing health from disease and classifying cardiac disease phenotypes. A total of 149 patients (n = 30 with no heart disease, n = 30 with LVH, n = 61 with hypertrophic cardiomyopathy (HCM) and n = 28 with cardiac amyloidosis) undergoing a CMR scan were included in this study. We extracted a total of 850 radiomic features and explored their value in disease classification. We applied principal component analysis and unsupervised clustering in exploratory analysis, and then machine learning for feature selection of the best radiomic features that maximized the diagnostic value for cardiac disease classification. The first three principal components of the T1 radiomics were distinctively correlated with cardiac disease type. Unsupervised hierarchical clustering of the population by myocardial T1 radiomics was significantly associated with myocardial disease type (chi2 = 55.98, p < 0.0001). After feature selection, internal validation and external testing, a model of T1 radiomics had good diagnostic performance (AUC 0.753) for multinomial classification of disease phenotype (normal vs. LVH vs. HCM vs. cardiac amyloid). A subset of six radiomic features outperformed mean native T1 values for classification between myocardial health vs. disease and HCM phenocopies (AUC of T1 vs. radiomics model, for normal: 0.549 vs. 0.888; for LVH: 0.645 vs. 0.790; for HCM 0.541 vs. 0.638; and for cardiac amyloid 0.769 vs. 0.840). We show that myocardial texture assessed by native T1 maps is linked to features of cardiac disease. Myocardial radiomic phenotyping could enhance the diagnostic yield of T1 mapping for myocardial disease detection and classification.