CT coronary fractional flow reserve based on artificial intelligence using different software: a repeatability study

作者：Jing Li, Zhenxing Yang, Zhenting Sun, Lei Zhao, Aishi Liu, Xing Wang, Qiyu Jin, Guoyu Zhang · 发表于：BMC Medical Imaging · 年份：2024 · DOI：10.1186/s12880-024-01465-4 · 被引用次数：6 · 研究领域：Cardiac Imaging and Diagnostics、Coronary Interventions and Diagnostics、Cardiovascular Health and Disease Prevention

OBJECTIVE: This study aims to assess the consistency of various CT-FFR software, to determine the reliability of current CT-FFR software, and to measure relevant influence factors. The goal is to build a solid foundation of enhanced workflow and technical principles that will ultimately improve the accuracy of measurements of coronary blood flow reserve fractions. This improvement is critical for assessing the level of ischemia in patients with coronary heart disease. METHODS: 103 participants were chosen for a prospective research using coronary computed tomography angiography (CCTA) assessment. Heart rate, heart rate variability, subjective picture quality, objective image quality, vascular shifting length, and other factors were assessed. CT-FFR software including K software and S software are used for CT-FFR calculations. The consistency of the two software is assessed using paired-sample t-tests and Bland-Altman plots. The error classification effect is used to construct the receiver operating characteristic curve. RESULTS: The CT-FFR measurements differed significantly between the K and S software, with a statistical significance of P < 0.05. In the Bland-Altman plot, 6% of the points (14 out of 216) fell outside the 95% consistency level. Single-factor analysis revealed that heart rate variability, vascular dislocation offset distance, subjective image quality, and lumen diameter significantly influenced the discrepancies in CT-FFR measurements between two software pro...