Characterizing the effects of noncontrast head CT reconstruction kernel and slice thickness parameters on the performance of an automated AI algorithm in the evaluation of ischemic stroke.

Purpose: There are multiple commercially available, Food and Drug Administration (FDA)-cleared, artificial intelligence (AI)-based tools automating stroke evaluation in noncontrast computed tomography (NCCT). This study assessed the impact of variations in reconstruction kernel and slice thickness on two outputs of such a system: hypodense volume and Alberta Stroke Program Early CT Score (ASPECTS). Approach: The NCCT series image data of 67 patients imaged with a CT stroke protocol were reconstructed with four kernels (H10s-smooth, H40s-medium, H60s-sharp, and H70h-very sharp) and three slice thicknesses (1.5, 3.0, and 5.0 mm) to create 1 reference condition (H40s/5.0 mm) and 11 nonreference conditions. The 12 reconstructions per patient were processed with a commercially available FDA-cleared software package that yields total hypodense volume (mL) and ASPECTS. A mixed-effect model was used to test the difference in hypodense volume, and an ordered logistic model was used to test the difference in e-ASPECTS. Results: Hypodense volume differences from the reference condition ranged from - 14.6 to 1.1 mL and were significant for all nonreference kernels (H10s p = 0.025 , H60s p < 0.001 , and H70h p < 0.001 ) and for thinner slices (1.5 mm p < 0.001 and 3.0 mm p = 0.002 ). e-ASPECTS was invariant to the nonreference kernels and slice thicknesses, with a mean difference ranging from - 0.1 to 0.5. No significant differences were found for any kernel or slice thickness (all p > 0.05 ). Conclusions: Automated hypodense volume measured with a commercially available, FDA-cleared software package is substantially impacted by reconstruction kernel and slice thickness. Conversely, automated ASPECTS is invariant to these reconstruction parameters.