Automated Measurement of Net Water Uptake From Baseline and Follow-Up CTs in Patients With Large Vessel Occlusion Stroke.
Automated Measurement of Net Water Uptake From Baseline and Follow-Up CTs in Patients With Large Vessel Occlusion Stroke.
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DOI：
10.3389/fneur.2022.898728
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发表时间：
2022
影响因子：
3.4
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中科院分区：
文献类型：
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量化卒中后脑水肿的程度和演变是一个重要但具有挑战性的目标。病灶净水摄取（NWU）是一种有前途的基于CT的水肿生物标志物，但其测量需要手动描绘梗死组织和对侧半球的镜像区域。我们实现了一个成像管道，能够自动分割梗死区域，并计算NWU从基线和随访CT的大血管闭塞（LVO）患者。使用去卷积算法从CT灌注图像中提取CT核心，而使用深度学习算法从非造影CT（NCCT）中分割随访CT上的梗死。将这些梗死掩模沿脑中线沿着翻转，以在NCCT的对侧半球中生成镜像区域; NWU计算为1减去区域之间的密度比，去除分割为CSF的体素，HU超出阈值20-80（正常半球和基线CT）和0-40（随访时的梗死区域）。将自动结果与使用手动绘制的梗死和基于ASPECTS感兴趣区域的方法获得的结果进行比较，该方法使用组内相关系数（ρ）对梗死和正常半球内的密度进行采样。对55例前循环LVO患者的系列CT（包括66例随访CT）进行了测试。使用自动核心的基线NWU为4.3%（IQR 2.6-7.3），与手动测量（ρ = 0.80，p < 0.0001）和ASPECTS（r =-0.60，p = 0.0001）相关。自动分割的梗死体积（中位数110 ml）与手动绘制的体积相关（ρ = 0.96，p < 0.0001），Dice相似系数中位数为0.83（IQR 0.72-0.90）。自动NWU为24.6%（IQR 20-27），与手动绘制梗死（ρ = 0.98）和基于采样的方法（ρ = 0.68，均p < 0.0001）的NWU高度相关。我们的结论是，这种自动成像管道能够准确地量化连续CT的梗死和NWU区域，并可用于研究大队列中风患者水肿的演变和影响。
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.
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