A Deep Learning Segmentation Pipeline for Cardiac T1 Mapping Using MRI Relaxation–based Synthetic Contrast Augmentation

Cardiac MRI relaxometry is clinically used to quantitatively characterize various cardiovascular conditions, such as myocardial infarction (1), myocarditis (2), amyloidosis (3), and cardiomyopathy (4). Single-slice and multislice short-axis myocardial T1-mapping protocols have enabled quantification of global and local tissue alterations, including edema and fibrosis, across these pathologic states (5). Furthermore, precontrast (native) T1 (T1native) and postcontrast T1 (T1post) mapping can be combined to provide an estimate of extracellular volume (ECV). Clinically, T1 and ECV can be used to differentiate cardiac abnormality and potentially grade disease severity and risk stratification (6,7). Currently, the measurement of segmental myocardial T1 and ECV requires manual delineation of the left ventricle (LV) myocardium, LV blood pool, and right ventricular (RV) insertion point (RVIP) in both native and postcontrast T1 maps.