Automated Estimation of Acute Infarct Volume from Noncontrast Head CT Using Image Intensity Inhomogeneity Correction

Abstract

Identification of early ischemic changes (EIC) on noncontrast head CT scans performed within the first few hours of stroke onset may have important implications for subsequent treatment, though early stroke is poorly delimited on these studies. Lack of sharp lesion boundary delineation in early infarcts precludes manual volume measures, as well as measures using edge-detection or region-filling algorithms. We wished to test a hypothesis that image intensity inhomogeneity correction may provide a sensitive method for identifying the subtle regional hypodensity which is characteristic of early ischemic infarcts. A digital image analysis algorithm was developed using image intensity inhomogeneity correction (IIC) and intensity thresholding. Two different IIC algorithms (FSL and ITK) were compared. The method was evaluated using simulated infarcts and clinical cases. For synthetic infarcts, measured infarct volumes demonstrated strong correlation to the true lesion volume (for 20% decreased density “infarcts,” Pearson r = 0.998 for both algorithms); both algorithms demonstrated improved accuracy with increasing lesion size and decreasing lesion density. In clinical cases (41 acute infarcts in 30 patients), calculated infarct volumes using FSL IIC correlated with the ASPECTS scores (Pearson r = 0.680) and the admission NIHSS (Pearson r = 0.544). Calculated infarct volumes were highly correlated with the clinical decision to treat with IV-tPA. Image intensity inhomogeneity correction, when applied to noncontrast head CT, provides a tool for image analysis to aid in detection of EIC, as well as to evaluate and guide improvements in scan quality for optimal detection of EIC.

Endovascular Thrombectomy for Large Ischemic Stroke Across Ischemic Injury and Penumbra Profiles

“…Thus, combining information from both imaging modalities best estimates the extent of ischemic injury and prognosis. Automated software to measure CT hypodensity is evolving, but, regardless, clinicians are able to recognize individuals in whom there are substantial noncontrast CT changes outside of the current CT perfusion core and account for these changes when assessing treatment decisions and prognosis. Imaging discordance between the 2 modalities was frequent and did not alter EVT treatment effect.…”

A Review on Computer Aided Diagnosis of Acute Brain Stroke

“…In the case of segmentation, a lack of a sharp boundary delineation hinders and delays the identification process. Cauley et al, tested a hypothesis and proved that image intensity inhomogeneity provides a sign for identifying the subtle hypo-density regionals which, in turn, is characteristic of ischemic infarct [56]. Ledig C et al, proposed a probabilistic framework for automatic segmentation of MRI using "multi-atlas label propagation" [57].…”

Deep Learning-Enabled Brain Stroke Classification on Computed Tomography營mages

“…On the other hand, CT findings associated with early ischemic changes are weak and difficult to detect on tomograms. For example, Akasaka et al reported that when MRI was used as the primary standard for ischemic stroke diagnosis, the average sensitivity for stroke diagnosis from noncontrast head computed tomography (NCCT) data was 26.5% among 14 radiologists [18]. Thus, an automated system assisting medical experts in the diagnosis of acute ischemic stroke on tomograms can be useful in clinical settings.…”