Detection of Intracerebral Hemorrhage Using Low-Field, Portable Magnetic Resonance Imaging in Patients With Stroke

• Published in: Stroke (1970) Vol. 54; no. 11; pp. 2832 - 2841
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: United States 01.11.2023
• Subjects: Cerebral Hemorrhage - complications; Humans; Ischemic Stroke - complications; Magnetic Resonance Imaging; Stroke - diagnosis; Tomography, X-Ray Computed; magnetic resonance imaging; cerebral hemorrhage; stroke; tomography; neuroimaging
• ISSN: 0039-2499; 1524-4628; 1524-4628
• DOI: 10.1161/STROKEAHA.123.043146

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.