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Objective <p>To evaluate the impact of target and whole vessel computational fluid dynamics (CFD) modeling strategies on CT-derived fractional flow reserve (CT-FFR).</p> Methods <p>The study enrolled patients suffering from moderate to severe intracranial atherosclerotic stenosis (ICAS) who underwent invasive FFR mearsurement and head CT angiography (CTA). CTA were used to reconstruct target and whole vessel CT-FFR models, and the calculation time of both models was recorded. Receiver operating characteristic (ROC) analysis assessed the diagnostic performance of two CT-FFR models with FFR ≤ 0.80 or 0.75 defining ischemia-specific stenosis.</p> Results <p>17 eligible patients (mean age, 58.5 ± 8.8 years, 13 males) were finally evaluated. The area under curve (AUC) of CT-FFR ≤ 0.80 measured based on target and whole vessel CT-FFR ≤ 0.80 was 1.000 (95% CI: 0.805-1.000) vs. 0.726 (95% CI: 0.461–0.909) (<i>P</i> &gt; 0.05). Similar results of CT-FFR ≤ 0.75 for detecting FFR ≤ 0.75 was obtained with the AUCs of 1.000 (95% CI: 0.805-1.000) and 0.917 (95% CI: 0.680–0.995) (<i>P</i> &gt; 0.05). Target Vessel CT-FFR (<i>r</i> = 0.936, <i>P</i> &lt; 0.001) and Whole Vessel CT-FFR (<i>r</i> = 0.938, <i>P</i> &lt; 0.001) had excellent correlation with FFR. Target Vessel CT-FFR showed a better agreement (mean difference: 0.022, 95% CI: -0.136-0.179) with invasive FFR and shorter modeling time than Whole Vessel CT-FFR ([32.65 ± 4.32&#xa0;min] versus [70.18 ± 13.82&#xa0;min], <i>P</i> &lt; 0.001).</p> Conclusions <p>Target vessel CT-FFR is recommended due to similar diagnostic performance and significantly shorter modeling time compared to whole vessel CT-FFR.</p>

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CT-derived fractional flow reserve in intracranial arterial stenosis: Performance comparison of target and whole cerebral vessel computational fluid dynamics models

  • 期刊论文

  • 发表日期：

    2026年7月31日

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  • Xinran Wang,
  • Yunfei Han,
  • Changsheng Zhou,
  • Jian Guo,
  • Fang Wang,
  • Qin Yin,
  • Zhao Shi,
  • Bin Hu,
  • Wusheng Zhu,
  • Longjiang Zhang

摘要

Objective

To evaluate the impact of target and whole vessel computational fluid dynamics (CFD) modeling strategies on CT-derived fractional flow reserve (CT-FFR).

Methods

The study enrolled patients suffering from moderate to severe intracranial atherosclerotic stenosis (ICAS) who underwent invasive FFR mearsurement and head CT angiography (CTA). CTA were used to reconstruct target and whole vessel CT-FFR models, and the calculation time of both models was recorded. Receiver operating characteristic (ROC) analysis assessed the diagnostic performance of two CT-FFR models with FFR ≤ 0.80 or 0.75 defining ischemia-specific stenosis.

Results

17 eligible patients (mean age, 58.5 ± 8.8 years, 13 males) were finally evaluated. The area under curve (AUC) of CT-FFR ≤ 0.80 measured based on target and whole vessel CT-FFR ≤ 0.80 was 1.000 (95% CI: 0.805-1.000) vs. 0.726 (95% CI: 0.461–0.909) (P > 0.05). Similar results of CT-FFR ≤ 0.75 for detecting FFR ≤ 0.75 was obtained with the AUCs of 1.000 (95% CI: 0.805-1.000) and 0.917 (95% CI: 0.680–0.995) (P > 0.05). Target Vessel CT-FFR (r = 0.936, P < 0.001) and Whole Vessel CT-FFR (r = 0.938, P < 0.001) had excellent correlation with FFR. Target Vessel CT-FFR showed a better agreement (mean difference: 0.022, 95% CI: -0.136-0.179) with invasive FFR and shorter modeling time than Whole Vessel CT-FFR ([32.65 ± 4.32 min] versus [70.18 ± 13.82 min], P < 0.001).

Conclusions

Target vessel CT-FFR is recommended due to similar diagnostic performance and significantly shorter modeling time compared to whole vessel CT-FFR.