Abstract
The hyperdense sign is a marker of thrombus in non-contrast computed tomography (NCCT) datasets. The aim of this work was to determine optimal Hounsfield unit (HU) thresholds for thrombus segmentation in thin-slice non-contrast CT (NCCT) and use these thresholds to generate 3D thrombus models. Patients with thin-slice baseline NCCT (≤2.5 mm) and MCA-M1 occlusions were included. CTA was registered to NCCT, and three regions of interest (ROIs) were placed in the NCCT, including: the thrombus, contralateral brain tissue, and contralateral patent MCA-M1 artery. Optimal HU thresholds differentiating the thrombus from non-thrombus tissue voxels were calculated using receiver operating characteristic analysis. Linear regression analysis was used to predict the optimal HU threshold for discriminating the clot only based on the average contralateral vessel HU or contralateral parenchyma HU. Three-dimensional models from 70 participants using standard (45 HU) and patient-specific thresholds were generated and compared to CTA clot characteristics. The optimal HU threshold discriminating thrombus in NCCT from other structures varied with a median of 51 (IQR: 49–55). Experts chose 3D models derived using patient-specific HU models as corresponding better to the thrombus seen in CTA in 83.8% (31/37) of cases. Patient-specific HU thresholds for segmenting the thrombus in NCCT can be derived using normal parenchyma. Thrombus segmentation using patient-specific HU thresholds is superior to conventional 45 HU thresholds.
Highlights
The hyperdense sign is a marker of intra-vascular thrombus in non-contrast computed tomography (NCCT) in patients with acute ischemic stroke
Wu et al reported that radiomics-based intracranial thrombus features, which require a detailed clot segmentation as the basis, were predictive of recanalization success with intravenous alteplase [2]
Among 315 patients enrolled in the ESCAPE study, 70 patients with thin slice NCCT
Summary
For example, showed that a hyperdense artery sign in NCCT measuring >8 mm in length had a negligible chance of recanalizing with intravenous alteplase alone [1]. Wu et al reported that radiomics-based intracranial thrombus features, which require a detailed clot segmentation as the basis, were predictive of recanalization success with intravenous alteplase [2]. Manual clot segmentation is still the standard procedure for image-based clot analyses in research studies, but this approach is not feasible for clinical applications due to its high time requirements. It can be challenging to identify the exact margins of the hyperdense clot, for example, due to signal noise and partial volume effects, especially in the case of thick-slice NCCT datasets, leading to considerable inter-observer differences in manual segmentations [3,4]. There is no well-justified single HU threshold described in the literature that has proven to enable a good thrombus differentiation from normal tissue parenchyma with high sensitivity and specificity
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