Abstract W P109: CT Angiography of Symptomatic Intracranial Atherosclerosis: Anatomical Predictors of Fractional Flow
Background: Current use of CT angiography (CTA) for intracranial atherosclerosis (ICAD) focuses solely on measurement of maximal degree in isolated stenosis, a parameter that poorly predicts subsequent stroke. Noninvasive modeling with computational fluid dynamics (CFD) measures hemodynamic impact of stenoses based on fractional flow. We tested a novel method to compare anatomy- and CFD-based hemodynamics.
Methods: A geometric mesh of ICAD lesions was derived from CTA. Semi-automated seed placement and centerline calculation across stenotic segments was used to automatically identify luminal stenoses, tandem lesions, plaque length and associated volumes. Fractional flow, the pressure drop across lesions, was calculated and compared to CFD-derived values using Ansys CFX and CFD-post, with boundary conditions for laminar flow.
Results: CTA of 73 patients (mean age 72.7 ± 10.2 years; 58% men) with recently symptomatic ICAD were analyzed by semi-automated anatomical measures and CFD. Lesions included 45 M1, 14 ICA, 11 Bas and 3 VA intracranial lesions with median stenosis of 62.0% (40-93). Median plaque length was 12.2 (2.9-29.9) mm. Total plaque volumes measured median 75.1 (19.2-325.3) mm3 with focal stenoses accounting for 40.0 (6.3-168.5) mm3. Pressure drop across the lesion calculated anatomically based on maximal stenosis averaged 34.7% (IQR 18.1-76.9) and 12.7% (IQR 5.0-40.2), the latter accounting for plaque length. These correlated moderately with fractional flow measured by CFD (ρ=0.545 and ρ=0.561, respectively). Plaque volumes of the entire lesion or the focal stenosis, the presence of tandem lesions or irregular surface morphology did not correlate with CFD-derived fractional flow.
Conclusions: Detailed anatomy of intracranial atherosclerotic lesions can be extracted by semi-automated techniques, revealing extensive heterogeneity. Clinical studies are needed to discern whether CTA anatomy or CFD-derived fractional flow measures predict recurrence of stroke in the territory.
Author Disclosures: D.S. Liebeskind: Research Grant; Significant; NIH/NINDS K24NS072272. Consultant/Advisory Board; Modest; Covidien, Stryker. X. Leng: None. F. Scalzo: None. M.S. Johnson: None. A.K. Fong: None. H.L. Ip: None. F. Fan: None. Y. Soo: None. T. Leung: None. L. Liu: None. K.S. Wong: None. E. Feldmann: None.
- © 2014 by American Heart Association, Inc.