Variation of computed tomography-derived extracellular volume fraction and the impact of protocol parameters: A systematic review and meta-analysis

Muthalaly, Rahul G.; Tan, Sean; Nelson, Adam J.; Abrahams, Timothy; Han, Donghee; Tamarappoo, Balaji K.; Dey, Damini; Nicholls, Stephen J.; Lin, Andrew; Nerlekar, Nitesh

doi:10.1016/j.jcct.2024.06.002

Journal of Cardiovascular Computed Tomography

2024

DOI: 10.1016/j.jcct.2024.06.002

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Variation of computed tomography-derived extracellular volume fraction and the impact of protocol parameters: A systematic review and meta-analysis

Rahul G. Muthalaly,

Sean Tan,

Adam J. Nelson

et al.

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Cited by 2 publications

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Myocardial extracellular volume by CT: Assessing the state of the union

Small

2024

Journal of Cardiovascular Computed Tomography

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Myocardial extracellular volume by CT: Assessing the state of the union

Small

2024

Journal of Cardiovascular Computed Tomography

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

Morioka,

Kato,

Onoma

et al. 2024

JCDD

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Background: 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. Methods: 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. Results: 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). Conclusions: 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.

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Variation of computed tomography-derived extracellular volume fraction and the impact of protocol parameters: A systematic review and meta-analysis

Cited by 2 publications

References 40 publications

Myocardial extracellular volume by CT: Assessing the state of the union

Myocardial extracellular volume by CT: Assessing the state of the union

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

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