Influence of deep learning image reconstruction and adaptive statistical iterative reconstruction-V on automated Alberta Stroke Program Early CT Score- evaluation

Purpose The Alberta Stroke Program Early CT Score (ASPECTS) and advances in CT reconstruction play an important role in the neurodiagnostic workflow. This study examines the effect of these reconstruction techniques on automated ASPECTS. Methods In a retrospective study, 173 patients (median age 79 years, 39% female) with suspected middle cerebral artery infarction underwent non-contrast CT scans reconstructed with Filtered Back Projection (FBP), ASIR-V (30% and 60%), and DLIR (low, medium, and high). Automated ASPECTS were analyzed, with FBP as the reference standard. Results Bland–Altman analysis revealed a mean bias of ASIR and DLIR underestimating ASPECTS compared to FBP, which was less pronounced for ASIR-V 30% (-0.057 ) and DLIR-L (-0.069) compared to ASIR-V 60% (-0.126), DLIR-M (-0.121), and DLIR-H (-0.086). The region with the highest overestimation, compared to FBP, was M3 (n = 23), and with the highest underestimation was the insular ribbon (n = 51). Regarding the ASPECTS < 6 threshold, most patients were re-classified from ASPECTS ≤ 5 to ASPECTS ≥ 6 with DLIR-M (n = 5 ), which also showed the strongest agreement with expert consensus (κ = 0.352). Conclusion Both ASIR-V and DLIR led to only a minor underestimation of ASPECTS compared to FBP. However, more patients were overestimated to ASPECTS ≥ 6, making them available for endovascular therapy, which was most pronounced for DLIR-M. DLIR-M also exhibited the highest agreement with expert consensus for automated ASPECTS. Careful selection of reconstruction parameters, as well as further optimization and standardization of these techniques, is therefore essential for broader application in stroke imaging.