医学
组内相关
红细胞压积
核医学
线性回归
再现性
协议限制
平淡——奥特曼情节
衰减校正
重复性
磁共振成像
放射科
正电子发射断层摄影术
统计
内科学
数学
心理测量学
临床心理学
作者
Muhammad Taha Hagar,W Garrison Moore,Milán Vecsey-Nagy,José Osoria-Velasquez,James Ira Griggers,Fabian Bamberg,Ákos Varga‐Szemes,Tilman Emrich
标识
DOI:10.1016/j.ejrad.2025.112321
摘要
Purpose To assess the feasibility and accuracy of synthetic hematocrit (Hct)-based myocardial extracellular volume (ECV) quantification using photon-counting detector (PCD)-CT late enhancement (LE) imaging, with cardiac MRI as the reference standard. Methods In this post-hoc analysis of a prospective study, patients underwent same-day cardiac MRI and PCD-CT LE imaging. MRI-based ECV (ECV MRI ) was computed using T1 mapping sequences before and after contrast administration. PCD-CT-based ECV (ECV PCD-CT ) was calculated using synthetic Hct derived from blood pool attenuation in virtual non-contrast (VNC) images of LE scans. Agreement between ECV MRI and ECV PCD-CT was evaluated using Bland-Altman analysis, Pearson correlation, and intraclass correlation coefficient (ICC) with absolute agreement. A linear regression model was applied to correct for systematic bias in ECV PCD-CT , generating bias-adjusted ECV values (ECV PCD-CT_adjusted ). Results A total of 27 patients (mean age 52.9 ± 17.2 years, 52 % female) were included. ECV PCD-CT systematically overestimated ECV MRI (42.1 ± 8.7 % vs. 33.8 ± 8.1 %, p < 0.001), with a mean bias of −8.3 percentage points and wide limits of agreement (−7.3 to 23.9). Correlation between ECV MRI and ECV PCD-CT was moderate (r = 0.56, p = 0.002), and ICC indicated poor reliability (ICC = 0.38). Bias correction using linear regression eliminated systematic error (ECV PCD-CT_adjusted : 35.0 ± 4.8 %, p = 0.373), with a reduced mean bias of −1.2 percentage points, but wide limits of agreement remained (−14.4 to 12.1). ICC improved slightly to 0.51, indicating moderate reliability. Conclusion While synthetic Hct-based ECV estimation using PCD-CT is technically feasible, it systematically overestimates MRI-derived ECV and demonstrates considerable inter-individual variability. Bias correction effectively eliminates systematic error, but the improvement in variability remains limited.
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