Mazurek Mercy H, Parasuram Nethra R, Peng Teng J, Beekman Rachel, Yadlapalli Vineetha, Sorby-Adams Annabel J, Lalwani Dheeraj, Zabinska Julia, Gilmore Emily J, Petersen Nils H, Falcone Guido J, Sujijantarat Nanthiya, Matouk Charles, Payabvash Sam, Sze Gordon, Schiff Steven J, Iglesias Juan Eugenio, Rosen Matthew S, de Havenon Adam, Kimberly W Taylor, Sheth Kevin N
Department of Neurology (M.H.M., N.R.P., T.J.P., R.B., V.Y., D.L., J.Z., E.J.G., N.H.P., G.J.F., A.d.H., K.N.S.), Yale School of Medicine, New Haven, CT.
Division of Neurocritical Care, Department of Neurology, Massachusetts General Hospital, Boston (A.J.S.-A., W.T.K.).
Stroke. 2023 Nov;54(11):2832-2841. doi: 10.1161/STROKEAHA.123.043146. Epub 2023 Oct 5.
Neuroimaging is essential for detecting spontaneous, nontraumatic intracerebral hemorrhage (ICH). Recent data suggest ICH can be characterized using low-field magnetic resonance imaging (MRI). Our primary objective was to investigate the sensitivity and specificity of ICH on a 0.064T portable MRI (pMRI) scanner using a methodology that provided clinical information to inform rater interpretations. As a secondary aim, we investigated whether the incorporation of a deep learning (DL) reconstruction algorithm affected ICH detection.
The pMRI device was deployed at Yale New Haven Hospital to examine patients presenting with stroke symptoms from October 26, 2020 to February 21, 2022. Three raters independently evaluated pMRI examinations. Raters were provided the images alongside the patient's clinical information to simulate real-world context of use. Ground truth was the closest conventional computed tomography or 1.5/3T MRI. Sensitivity and specificity results were grouped by DL and non-DL software to investigate the effects of software advances.
A total of 189 exams (38 ICH, 89 acute ischemic stroke, 8 subarachnoid hemorrhage, 3 primary intraventricular hemorrhage, 51 no intracranial abnormality) were evaluated. Exams were correctly classified as positive or negative for ICH in 185 of 189 cases (97.9% overall accuracy). ICH was correctly detected in 35 of 38 cases (92.1% sensitivity). Ischemic stroke and no intracranial abnormality cases were correctly identified as blood-negative in 139 of 140 cases (99.3% specificity). Non-DL scans had a sensitivity and specificity for ICH of 77.8% and 97.1%, respectively. DL scans had a sensitivity and specificity for ICH of 96.6% and 99.3%, respectively.
These results demonstrate improvements in ICH detection accuracy on pMRI that may be attributed to the integration of clinical information in rater review and the incorporation of a DL-based algorithm. The use of pMRI holds promise in providing diagnostic neuroimaging for patients with ICH.
神经影像学对于检测自发性非创伤性脑出血（ICH）至关重要。近期数据表明，可使用低场磁共振成像（MRI）对ICH进行特征描述。我们的主要目标是采用一种能提供临床信息以指导评估者解读的方法，研究0.064T便携式MRI（pMRI）扫描仪检测ICH的敏感性和特异性。作为次要目标，我们研究了深度学习（DL）重建算法的纳入是否会影响ICH检测。
2020年10月26日至2022年2月21日期间，pMRI设备被部署在耶鲁纽黑文医院，用于检查出现中风症状的患者。三名评估者独立评估pMRI检查。向评估者提供图像以及患者的临床信息，以模拟实际使用场景。金标准为最接近的传统计算机断层扫描或1.5/3T MRI。敏感性和特异性结果按DL和非DL软件分组，以研究软件进展的影响。
共评估了189例检查（38例ICH、89例急性缺血性中风、8例蛛网膜下腔出血、3例原发性脑室内出血、51例无颅内异常）。189例中有185例检查对ICH的阳性或阴性分类正确（总体准确率97.9%）。38例中有35例正确检测到ICH（敏感性92.1%）。140例缺血性中风和无颅内异常病例中有139例被正确判定为无血（特异性99.3%）。非DL扫描对ICH的敏感性和特异性分别为77.8%和97.1%。DL扫描对ICH的敏感性和特异性分别为96.6%和99.3%。
这些结果表明，pMRI检测ICH的准确性有所提高，这可能归因于评估者审查中临床信息的整合以及基于DL算法的纳入。pMRI在为ICH患者提供诊断性神经影像学检查方面具有前景。
