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

Abstract

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.

Impact of dose reduction and iterative model reconstruction on multi-detector CT imaging of the brain in patients with suspected ischemic stroke

“…Even though the differences regarding imaging quality and conspicuity of ischemic areas are minor, the inter-reader agreement in the blinded rating of both raters was substantial to almost perfect for most evaluated items and scans, except for evaluations of GM/WM differentiation using SD imaging data with model-based iterative reconstruction. In this regard, previous research has already suggested that the performance of human readers for assessing ischemic demarcation can depend on the algorithm used for MDCT image reconstruction, with a trend towards better agreement for more established reconstruction algorithms (i.e., hybrid algorithms) with the experience of the reader 41 . Thus, a comparable result may be present for ratings of GM/WM differentiation in SD imaging data with model-based iterative reconstruction, which might be interpreted as an analogous trend to higher variation between readers for the more recently introduced model-based iterative image reconstruction algorithm over the more established hybrid algorithm.…”

Validation of two automated ASPECTS software on non-contrast computed tomography scans of patients with acute ischemic stroke

“…NBC software is now available at our hospital for automatic calculation of ASPECTS. In addition, there are available options for various parameters of NCCT images (e.g., slice thickness and reconstruction algorithms) (7,8). Thinner slices and iterative reconstruction have been shown to improve the reliability and performance of automated ASPECTS (7)(8)(9).…”

“…In addition, there are available options for various parameters of NCCT images (e.g., slice thickness and reconstruction algorithms) (7,8). Thinner slices and iterative reconstruction have been shown to improve the reliability and performance of automated ASPECTS (7)(8)(9). However, some studies have found no significant difference in ASPECTS automatically derived from images with different slice thicknesses (1 mm vs. 2.5 mm, ≤3 mm vs. 3-6 mm) (10,11).…”

“…Validation studies of automated ASPECTS software also apply different reference standards for ASPECTS (7)(8)(9)(10)(11). Different reference standards have their inherent limitations.…”

“…algorithm (current study: hybrid iterative reconstruction, level 3; Austein's study (11): hybrid iterative reconstruction, level 2; Loffler's study (8): iterative model reconstruction). Reconstruction algorithm can improve the image quality of brain NCCT and improve the accuracy of ASPECTS.…”