Automated Measurement of Net Water Uptake From Baseline and Follow-Up CTs in Patients With Large Vessel Occlusion Stroke

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Automated Measurement of Net Water Uptake From Baseline and Follow-Up CTs in Patients With Large Vessel Occlusion Stroke

Atul Kumar¹, Yasheng Chen¹, Aaron Corbin², Ali Hamzehloo¹, Amin Abedini³, Zeynep Vardar⁴, Grace Carey¹, Kunal Bhatia¹, Laura Heitsch⁵, Jamal J. Derakhshan³, Jin-Moo Lee¹ and Rajat Dhar^1*

• ¹Department of Neurology, Washington University in St. Louis School of Medicine, Saint Louis, MO, United States
• ²Saint Louis University School of Medicine, Saint Louis, MO, United States
• ³Department of Radiology, Washington University in St. Louis School of Medicine, Saint Louis, MO, United States
• ⁴Department of Radiology, University of Massachusetts Medical School, Worcester, MA, United States
• ⁵Department of Emergency Medicine, Washington University in St. Louis School of Medicine, Saint Louis, MO, United States

Quantifying the extent and evolution of cerebral edema developing after stroke is an important but challenging goal. Lesional net water uptake (NWU) is a promising CT-based biomarker of edema, but its measurement requires manually delineating infarcted tissue and mirrored regions in the contralateral hemisphere. We implement an imaging pipeline capable of automatically segmenting the infarct region and calculating NWU from both baseline and follow-up CTs of large-vessel occlusion (LVO) patients. Infarct core is extracted from CT perfusion images using a deconvolution algorithm while infarcts on follow-up CTs were segmented from non-contrast CT (NCCT) using a deep-learning algorithm. These infarct masks were flipped along the brain midline to generate mirrored regions in the contralateral hemisphere of NCCT; NWU was calculated as one minus the ratio of densities between regions, removing voxels segmented as CSF and with HU outside thresholds of 20–80 (normal hemisphere and baseline CT) and 0–40 (infarct region on follow-up). Automated results were compared with those obtained using manually-drawn infarcts and an ASPECTS region-of-interest based method that samples densities within the infarct and normal hemisphere, using intraclass correlation coefficient (ρ). This was tested on serial CTs from 55 patients with anterior circulation LVO (including 66 follow-up CTs). Baseline NWU using automated core was 4.3% (IQR 2.6–7.3) and correlated with manual measurement (ρ = 0.80, p < 0.0001) and ASPECTS (r = −0.60, p = 0.0001). Automatically segmented infarct volumes (median 110-ml) correlated to manually-drawn volumes (ρ = 0.96, p < 0.0001) with median Dice similarity coefficient of 0.83 (IQR 0.72–0.90). Automated NWU was 24.6% (IQR 20–27) and highly correlated to NWU from manually-drawn infarcts (ρ = 0.98) and the sampling-based method (ρ = 0.68, both p < 0.0001). We conclude that this automated imaging pipeline is able to accurately quantify region of infarction and NWU from serial CTs and could be leveraged to study the evolution and impact of edema in large cohorts of stroke patients.

Introduction

A major consequence of brain ischemia is the development of cerebral edema. This water accumulation within and around the injured tissue leads to brain swelling, raising compartmental pressure and eventually leading to midline shift and herniation. The development of malignant cerebral edema represents the greatest source of mortality in the acute period after ischemic stroke, especially for strokes due to large vessel occlusion (LVO) (1). As key mediators remain incompletely understood, few interventions currently exist to mitigate cerebral edema (2). One of the major limitations in studying edema is the need for an accurate means of quantifying its formation in the early stages after stroke (3, 4). Midline shift is a crude measure that does not adequately capture edema as it develops over the first 24–48 h after stroke, but only captures its delayed and decompensated phenotype. Furthermore, labeling edema only when it leads to deterioration (i.e., malignant edema) obscures a continuum of injury severity that is seen across almost all LVO stroke patients (5).

One of the hallmarks of evolving brain edema is tissue hypoattenuation (6). This can be captured by the progressively decreasing density (measured in Hounsfield Units, HU) of infarcted tissue on non-contrast computed tomography (NCCT) imaging. NCCT is readily available and routinely performed in almost all stroke patients, both acutely on presentation and frequently at follow-up. It affords an accessible means of serially assessing edema as it develops in the days after stroke. However, measurement of the total lesional hypodensity volume encompasses both infarcted tissue and associated edema, with relative proportions varying across patients (5, 7). A recent imaging method has been proposed to disentangle the contribution of edema to subacute lesion volume and quantify the progression edema after stroke (8). Net water uptake (NWU) evaluates the relative density of the ischemic tissue compared to a contralateral homologous region; increasing NWU on admission NCCT has been associated with longer time from stroke onset to imaging and poor collateral status (9, 10). NWU has also exhibited promise in quantifying edema progression, rising more in those with malignant outcomes and in those without successful recanalization (11, 12). Therefore, it has emerged as one of the most promising biomarkers of edema after stroke, with a wide array of potential applications across LVO cohorts (13).

However, implementation of NWU measurement from serial CTs in large stroke cohorts faces several challenges. The principal challenge is that its assessment is dependent on identification and delineation of the area of early infarction on acute and subacute CTs. As this region is not usually clearly visible on baseline NCCT within a few hours of stroke onset, most studies measuring early NWU have relied on CT perfusion (CTP) images to visually guide manual delineation of core infarct. In some studies where CTP was not available, NWU was estimated by measuring density within regions-of-interest (ROIs) placed within ASPECTS regions exhibiting early hypoattenuation and matched regions in the contralateral hemisphere (14). Measurement of NWU on follow-up NCCT requires manually outlining the visible region of infarction and flipping this manual ROI to create a homologous normal region for density assessment. This approach is time-consuming, subject to variability, and makes studying edema in large cohorts with NWU, although attractive in theory, challenging to perform in practice. Our objective was to develop an accurate means of automatically extracting infarct regions and measuring NWU from both baseline and follow-up CTs of LVO stroke patients. This imaging algorithm could then be leveraged to accelerate research into edema using larger cohorts of stroke patients (15).

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