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

• Published in: Journal of cardiovascular development and disease 2024-10, Vol.11 (10), p.304
• Main Authors: Morioka, Tsubasa, Kato, Shingo, Onoma, Ayano, Izumi, Toshiharu, Sakano, Tomokazu, Ishikawa, Eiji, Sawamura, Shungo, Yasuda, Naofumi, Nagase, Hiroaki, Utsunomiya, Daisuke
• Format: Article
• Language: English
• Subjects: Amyloidosis; Aortic stenosis; Blood; Blood tests; Cardiac patients; computed tomography; Coronary vessels; CT imaging; Data collection; deep leaning reconstruction; Deep learning; Laboratories; Magnetic resonance imaging; Medical examination; myocardial fibrosis; Regression analysis; Reproducibility; Software; Statistical analysis; synthetic ECV
• ISSN: 2308-3425; 2308-3425
• DOI: 10.3390/jcdd11100304

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: = 40, age 71 ± 12 years, 24 males; validation cohort: = 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, < 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.