Synthetic Extracellular Volume in Cardiac Magnetic Resonance Without Blood Sampling: a Reliable Tool to Replace Conventional Extracellular Volume.

Background: The calculation of extracellular volume (ECV) in cardiac magnetic resonance requires hematocrit, limiting its applicability in clinical practice. Based on the linear relationship between hematocrit and blood T1 relaxivity, a synthetic ECV could be estimated without a blood sample. We aim to develop and test regression models for synthetic ECV without blood sampling in 1.5-T and 3.0-T scanners.

Methods: A total of 1101 subjects who underwent cardiac magnetic resonance scanning with native and postcontrast T1 mapping and venous hematocrit within 24 hours were retrospectively enrolled. Subjects were randomly split into derivation (n=550) and validation (n=551) subgroups for each scanner. Different regression models were derived controlling for sex, field strength, and left ventricle/right ventricle blood pool and validated in the validation group. We performed additional validation analyses in subgroups of patients with histological validation (n=17), amyloidosis (n=29), anemia (n=185), and reduced ejection fraction (n=322).

Results: In the derivation group, 8 specific models and 2 common estimate models were derived. In the validation group, using specific models, synthetic ECV had high agreement with conventional ECV (R, 0.87; <0.0001 and R, 0.88, <0.0001; -0.16% and -0.10%, left ventricle and right ventricle model, respectively). Common models also performed well (R, 0.88; <0.0001 and R, 0.89, <0.0001; -0.21% and -0.18%, left ventricle and right ventricle model, respectively). Histological validation demonstrated equal performance of synthetic and measured ECV. Synthetic ECV as calculated by the common model showed a bias in the anemia cohort significantly reduced by the specific model (-2.45 to -1.28, right ventricle common and specific model, respectively).

Conclusions: Synthetic ECV provided a promising way to calculate ECV without blood sampling. Specific models could provide the most accurate value, while common models could be more suitable in routine clinical practice because of their simplicity while maintaining adequate accuracy.