Key result
Fully automated ECV matches manual quantification with an absolute difference of ~1.2%, but falters at high hematocrit.
Why the study?
Automated post-processing software has been developed to quantify CMR native T1 and extracellular volume (ECV) using synthetic hematocrit (Hct), but validation studies of these automated measurements remain limited.
Does fully automated ECV quantification integrating automated myocardial T1 analysis and synthetic hematocrit agree with manual ECV calculation using venous hematocrit on 3T cardiac MRI in adults with low-to-intermediate ASCVD risk?
Cross-Sectional (n=65)
Blinded image observers
No
Does fully automated ECV quantification integrating automated myocardial T1 analysis and synthetic hematocrit agree with manual ECV calculation using venous hematocrit on 3T cardiac MRI in adults with low-to-intermediate ASCVD risk?
Fully automated 3T CMR ECV quantification using synthetic hematocrit strongly agrees with manual venous-hematocrit ECV in patients with normal hematocrit (35-45%), eliminating the need for blood sampling, but is unreliable when hematocrit exceeds 45%.
Answers are grounded in this paper and cite their sources.
May support automated ECV without venipuncture at normal Hct; leaves open reliability at Hct >45% pending prospective validation.
Background: Automated post-processing software has been developed to quantify various cardiac magnetic resonance (CMR) parameters, including native T1 and extracellular volume (ECV) using synthetic hematocrit (Hct). However, validation studies of these automated measurements remain limited. We aimed to validate a fully automated workflow integrating myocardial T1 analysis and synthetic Hct for ECV quantification on 3T CMR, using manual ECV calculation based on venous Hct as the reference method. Methods: This retrospective, cross-sectional study included 65 adults with low-to-intermediate risk of atherosclerotic cardiovascular disease who underwent stress 3T CMR. Manual ECV was calculated using manually contoured myocardial T1 values and venous Hct obtained within 7 days of CMR. Automated analysis used Myomics-T1 software (Phantomics, Korea) to automatically generate myocardial and blood-pool T1 values and contours, with synthetic Hct derived from a regression model developed in an independent cohort of 450 participants. Correlation and agreement were assessed using Pearson correlation, intraclass correlation coefficients (ICC), and Bland-Altman analysis. Results: 27.8%±4.6%). Synthetic Hct correlated with venous Hct (r=0.78; ICC =0.76) but demonstrated proportional bias. Agreement between synthetic Hct-derived and manual ECV was better within the normal Hct range (35-45%) but substantially reduced at Hct >45% (ICC =0.04). Conclusions: Fully automated ECV quantification demonstrated strong correlation and agreement with the manual method within the normal Hct range. However, synthetic Hct-derived ECV was less reliable at abnormal Hct levels, particularly at Hct >45%. Further external validation and refinement of the synthetic Hct model are warranted to support broader clinical application.
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Kositanurit et al. (2026) conducted a cross-sectional in Suspected coronary artery disease with low-to-intermediate risk of atherosclerotic cardiovascular disease (n=65). Fully automated ECV quantification (Myomics-T1 software) integrating automated myocardial T1 analysis and synthetic hematocrit vs. Manual ECV calculation using manually contoured myocardial T1 values (syngo.via) and venous hematocrit was evaluated on Agreement and mean difference in extracellular volume (ECV, %) between fully automated and manual methods (MD -1.24% (ICC 0.93, r=0.91), 95% CI -1.76 to -0.72). Fully automated ECV quantification showed excellent agreement with manual venous-Hct ECV overall (ICC 0.93, MD -1.24%), though agreement dropped substantially at Hct >45% (ICC 0.04).
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