Improvement of Quantification of Myocardial Synthetic ECV with Second-Generation Deep Learning Reconstruction

The utility of synthetic ECV, which does not require hematocrit values, has been reported; however, high-quality CT images are essential for accurate quantification. Second-generation Deep Learning Reconstruction (DLR) enables low-noise and high-resolution cardiac CT images. The aim of this study is to compare the differences among four reconstruction methods (hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), DLR, and second-generation DLR) in the quantification of synthetic ECV.

We retrospectively analyzed 80 patients who underwent cardiac CT scans, including late contrast-enhanced CT (derivation cohort: n = 40, age 71 ± 12 years, 24 males; validation cohort: n = 40, age 67 ± 11 years, 25 males). In the derivation cohort, a linear regression analysis was performed between the hematocrit values from blood tests and the CT values of the right atrial blood pool on non-contrast CT. In the validation cohort, synthetic hematocrit values were calculated using the linear regression equation and the right atrial CT values from non-contrast CT. The correlation and mean difference between synthetic ECV and laboratory ECV calculated from actual blood tests were assessed.

Synthetic ECV and laboratory ECV showed a high correlation across all four reconstruction methods (R ≥ 0.95, p < 0.001). The bias and limit of agreement (LOA) in the Bland-Altman plot were lowest with the second-generation DLR (hybrid IR: bias = -0.21, LOA: 3.16; MBIR: bias = -0.79, LOA: 2.81; DLR: bias = -1.87, LOA: 2.90; second-generation DLR: bias = -0.20, LOA: 2.35).

Synthetic ECV using second-generation DLR demonstrated the lowest bias and LOA compared to laboratory ECV among the four reconstruction methods, suggesting that second-generation DLR enables more accurate quantification.