Deep Learning Virtual Contrast‐Enhanced T1 Mapping for Contrast‐Free Myocardial Extracellular Volume Assessment

Abstract

Background The acquisition of contrast‐enhanced T1 maps to calculate extracellular volume (ECV) requires contrast agent administration and is time consuming. This study investigates generative adversarial networks for contrast‐free, virtual extracellular volume (vECV) by generating virtual contrast‐enhanced T1 maps. Methods and Results This retrospective study includes 2518 registered native and contrast‐enhanced T1 maps from 1000 patients who underwent cardiovascular magnetic resonance at 1.5 Tesla. Recent hematocrit values of 123 patients (hold‐out test) and 96 patients from a different institution (external evaluation) allowed for calculation of conventional ECV. A generative adversarial network was trained to generate virtual contrast‐enhanced T1 maps from native T1 maps for vECV creation. Mean and SD of the difference per patient (ΔECV) were calculated and compared by permutation of the 2‐sided t test with 10 000 resamples. For ECV and vECV, differences in area under the receiver operating characteristic curve (AUC) for discriminating hold‐out test patients with normal cardiovascular magnetic resonance versus myocarditis or amyloidosis were tested with Delong's test. ECV and vECV showed a high agreement in patients with myocarditis (ΔECV: hold‐out test, 2.0%±1.5%; external evaluation, 1.9%±1.7%) and normal cardiovascular magnetic resonance (ΔECV: hold‐out test, 1.9%±1.4%; external evaluation, 1.5%±1.2%), but variations in amyloidosis were higher (ΔECV: hold‐out test, 6.2%±6.0%; external evaluation, 15.5%±6.4%). In the hold‐out test, ECV and vECV had a comparable AUC for the diagnosis of myocarditis (ECV AUC, 0.77 versus vECV AUC, 0.76; P=0.76) and amyloidosis (ECV AUC, 0.99 versus vECV AUC, 0.96; P=0.52). Conclusions Generation of vECV on the basis of native T1 maps is feasible. Multicenter training data are required to further enhance generalizability of vECV in amyloidosis.

Keywords

• cardiovascular magnetic resonance
• deep learning
• extracellular volume
• generative adversarial networks
• T1 mapping