Improved Reliability of Automated ASPECTS Evaluation Using Iterative Model Reconstruction from Head CT Scans

BACKGROUND AND PURPOSE Iterative model reconstruction (IMR) has shown to improve computed tomography (CT) image quality compared hybrid iterative (HIR). Alberta Stroke Program Early CT Score (ASPECTS) assessment in early stroke is particularly dependent on high-image quality. Purpose of this study was investigate the reliability ASPECTS assessed by humans and software based HIR IMR, respectively. METHODS Forty-seven consecutive patients with acute anterior circulation large vessel occlusions (LVOs) successful endovascular thrombectomy were included. three neuroradiologists (one attending, two residents) automated noncontrast axial (iDose4; 5 mm) IMR (5 0.9 mm). Two expert determined consensus reading using all available data including MRI. Agreement between four raters (three humans, one software) square-weighted kappa (κ). RESULTS Human achieved moderate almost perfect agreement (κ = .557-.845) reading. The attending showed for mm (κHIR .845), while residents had mostly substantial agreements without clear trends across reconstructions. Software consensus, increasing slice thickness .751, κIMR .777, κIMR0.9 .814). Agreements inversely declined these reconstructions .845, .763, .681). CONCLUSIONS rating good different performed best algorithms they most experience (HIR attending). Automated benefits from higher resolution better contrasts thickness.