Identifying perfusion deficits on CT perfusion images using temporal similarity perfusion (TSP) mapping

Objectives: Deconvolution-derived maps of CT perfusion (CTP) data may be confounded by transit delays. We propose temporal similarity perfusion (TSP) analysis to decrease CTP maps’ dependence on transit times and investigate its sensitivity to detect perfusion deficits. Methods: CTP data of acute stroke patients obtained within 9 h of symptom onset was analyzed using a delay-insensitive singular value decomposition method and with TSP. The TSP method applies an iterative process whereby a pixel’s highest Pearson’s R value is obtained through comparison of a pixel’s time-shifted signal density time-series curve and the average whole brain signal density time-series curve. Our evaluation included a qualitative and quantitative rating of deconvolution maps (MTT, CBV, and TTP), of TSP maps, and of follow-up CT. Results: Sixty-five patients (mean 68 (SD 13) years, 34 male) were included. A perfusion deficit was identified in 90%, 86%, 65%, and 84% of MTT, TTP, CBV, and TSP maps. The agreement of MTT, TTP, and TSP with CT follow-up was comparable but noticeably lower for CBV. CBV had the best relationship with final infarct volume (R² = 0.77, p < 0.001), followed by TSP (R² = 0.63, p < 0.001). Intra-rater agreement of an inexperienced reader was higher for TSP than for CBV/MTT maps (kappa’s of 0.79–0.84 and 0.63–0.7). Inter-rater agreement for experienced readers was comparable across maps. Conclusions: TSP maps are easier to interpret for inexperienced readers. Perfusion deficits detected by TSP are smaller which may suggest less dependence on transit delays although more investigation is required. Key Points: • Temporal similarity perfusion mapping assesses CTP data based on similarities in signal time-curves. • TSP maps are comparable in perfusion deficit detection to deconvolution maps. • TSP maps are easier to interpret for inexperienced readers.