Determination of the Middle Cerebral Artery Occlusion Length in Acute Stroke: Contribution of 4D CT Angiography and Importance for Thrombolytic Efficacy Prediction
Abstract
Purpose To assess the benefit of 4D-CT angiography (4D-CTA) in determination and precise measurement of middle cerebral artery (MCA) occlusion in comparison to CTA. Possible relationship of measured occlusion lengths with recanalization after intravenous thrombolysis was analysed as a second objective.
Methods Detailed evaluation of complete MCA occlusions in 80 patients before intravenous thrombolysis using temporal maximum intensity projection (tMIP) dataset, calculated from 4D-CTA and conventional single-phase CTA was performed. Further, manual measurement technique was compared to results of semiautomatic procedure (vessel analysis) as reference.
Statistical analysis of correlation between MCA occlusion length and IVT efficacy (24 h recanalization rate according modified Thrombolysis In Myocardial Infarction criteria-mTIMI) was performed. Results The distal end of occlusion was identified in all patients using tMIP, but only in 48 patients (60 %) using CTA.
The manual measurement method was not statistically different and well correlated with reference tMIP-vessel analysis. (15.4 vs. 16.3 mm; p = 0.434; r = 97). In measurable occlusions by CTA, no significant difference was proved in manually measured lengths using tMIP and CTA (14.5 vs. 13.3 mm; p = 0.089).
Favorable recanalization (mTIMI 2-3) was achieved in 37 patients (47 %). Length of occlusion in M1 segment (p = 0.002) and M2 segment involvement (p = 0.017) were proved as independent negative predictors of recanalization.
Using receiver operating characteristics analysis, the cutoff length of the M1 segment occlusion for favorable recanalization was found to be 12 mm. Conclusion The feasibility of MCA occlusion assessment using tMIP datasets and benefit over conventional CTA were confirmed.
The manual measurement method was proved as feasible and simple with good correlation to reference semiautomatic analysis.