Identifying Thrombus on Non-Contrast CT in Patients with Acute Ischemic Stroke

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.

The prognostic value of extracranial vascular characteristics on procedural duration and revascularization success in endovascularly treated acute ischemic stroke patients

“…Hard or resistant occlusions can be another reason for unsuccessful revascularization, 36 but whether characteristics on pre-intervention CT, such as hyperdense vessel sign, are related to treatment success is not clear 37 and thrombus density measures are only recently improved for use in clinical practice. 38 Both factors were therefore outside the scope of our analyses. Strengths of our study include the use of extracranial vascular characteristics available prior to EVT on preintervention CTA.…”

Automated Segmentation of Intracranial Thrombus on NCCT and CTA in Patients with Acute Ischemic Stroke Using a Coarse-to-Fine Deep Learning Model

“…7 Santos et al 27 developed a semiautomated region-growing segmentation method that was limited by a low observer agreement and variability in thrombus density. Qazi et al 12 used linear regression to build statistical models to predict patient-specific optimal Hounsfield unit thresholds, which replaced a universal single Hounsfield unit threshold for thrombus segmentation favored by Riedel et al 28 However, these thrombus density threshold-based methods are subject to image-intensity variability, and their generalizability is a concern. Lucas et al 29 proposed a cascaded neural network to segment thrombi.…”

“…These challenges imply that the traditional model-based or thresholding-based segmentation methods may not be able to achieve accurate or acceptable results. [9][10][11][12] To the best of our knowledge, there are very few established approaches to automatically segment intracranial thrombi on CT images. This study, therefore, aims to develop and externally validate an automated thrombus-segmentation approach on NCCT and CTA images in patients with acute stroke presenting with intracranial vessel occlusion.…”

Thrombus Density in Acute Basilar Artery Occlusion Depends on Slice Thickness and the Method of Manual Thrombus Delineation

“…In the future, automatic thrombus detection and measurement [ 36 ] and machine learning algorithms [ 37 ] may aid the human reader. Recently, Qazi and co-workers presented a method to perform a complete 3D model of the thrombus, which might also be implemented into clinical routines [ 38 ].…”