Improved Reliability of Automated ASPECTS Evaluation Using Iterative Model Reconstruction from Head CT Scans. Issue 2 (9th January 2021)

• Record Type: Journal Article
• Authors: Löffler, Maximilian T.; Sollmann, Nico; Mönch, Sebastian; Friedrich, Benjamin; Zimmer, Claus; Baum, Thomas; Maegerlein, Christian; Kirschke, Jan S.
• Is Part Of: Journal of neuroimaging. Volume 31:Issue 2(2021)
• Page Start: 341
• Page End: 347
• Publication Date: 2021-01-09
• Subjects: Cerebrovascular disease and stroke -- computer‐assisted image analysis -- iterative image reconstruction -- middle cerebral artery infarction -- multidetector computed tomography; Diagnostic imaging -- Periodicals; Nervous system -- Diseases -- Diagnosis -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Système nerveux -- Maladies -- Diagnostic -- Périodiques; Imagerie médicale; Neuroimagerie; Neurologie; Système nerveux; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.804754
• DOI: 10.1111/jon.12810
• Languages: English
• ISSNs: 1051-2284

Abstract

ABSTRACT: BACKGROUND AND PURPOSE: Iterative model reconstruction (IMR) has shown to improve computed tomography (CT) image quality compared to hybrid iterative reconstruction (HIR). Alberta Stroke Program Early CT Score (ASPECTS) assessment in early stroke is particularly dependent on high‐image quality. Purpose of this study was to investigate the reliability of ASPECTS assessed by humans and software based on HIR and IMR, respectively. METHODS: Forty‐seven consecutive patients with acute anterior circulation large vessel occlusions (LVOs) and successful endovascular thrombectomy were included. ASPECTS was assessed by three neuroradiologists (one attending, two residents) and by automated software in noncontrast axial CT with HIR (iDose4; 5 mm) and IMR (5 and 0.9 mm). Two expert neuroradiologists determined consensus ASPECTS reading using all available image data including MRI. Agreement between four raters (three humans, one software) and consensus were compared using square‐weighted kappa ( κ ). RESULTS: Human raters achieved moderate to almost perfect agreement ( κ = .557‐.845) with consensus reading. The attending showed almost perfect agreement for 5 mm HIR ( κ HIR = .845), while residents had mostly substantial agreements without clear trends across reconstructions. Software had substantial to almost perfect agreement with consensus, increasing with IMR 5 and 0.9 mm slice thickness ( κ HIR = .751, κ IMR = .777, and κ IMR0.9 = .814). Agreements inversely declined for these reconstructions for the attending ( κ HIR = .845, κ IMR = .763, and κ IMR0.9 = .681). CONCLUSIONS: Human and software rating showed good reliability of ASPECTS across different CT reconstructions. Human raters performed best with the reconstruction algorithms they had most experience with (HIR for the attending). Automated software benefits from higher resolution with better contrasts in IMR with 0.9 mm slice thickness.