Portable, low-field magnetic resonance imaging enables highly accessible and dynamic bedside evaluation of ischemic stroke

Background and Purpose A significant proportion of strokes occur while patients are hospitalized for other reasons. Numerous stroke scales have been developed and validated for use in pre-hospital and emergency department settings, and there is growing interest to adapt these scales for use in the inpatient setting. We aimed to validate existing stroke scales for inpatient stroke codes. Methods We retrospectively reviewed charts from inpatient stroke code activations at an urban academic medical center from January 2016 through December 2018. Receiver operating characteristic analysis was performed for each specified stroke scale including NIHSS, FAST, BE-FAST, 2CAN, FABS, TeleStroke Mimic, and LAMS. We also used logistic regression to identify independent predictors of stroke and to derive a novel scale. Results Of the 958 stroke code activations reviewed, 151 (15.8%) had a final diagnosis of ischemic or hemorrhagic stroke. The area under the curve (AUC) of existing scales varied from .465 (FABS score) to .563 (2CAN score). Four risk factors independently predicted stroke: (1) recent cardiovascular procedure, (2) platelet count less than 50 × 109 per liter, (3) gaze deviation, and (4) presence of unilateral leg weakness. Combining these 4 factors into a new score yielded an AUC of .653 (95% confidence interval [CI] .604-.702). Conclusion This study suggests that currently available stroke scales may not be sufficient to differentiate strokes from mimics in the inpatient setting. Our data suggest that novel approaches may be required to help with diagnosis in this unique population.

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“…[22] and MRI Ref. [23, 24]), electroencephalography, 25 transcranial Doppler, 26 and near-infrared spectroscopy 27 needs further study.…”

Section: Discussionmentioning

confidence: 99%

Validating Existing Scales for Identification of Acute Stroke in an Inpatient Setting

Sari

Velez

Muntz

et al. 2023

The Neurohospitalist

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“…Prior work has demonstrated a sensitivity and specificity of 80.4% and 96.6% for ICH and 93% and 96% for MLS when compared to standard-of-care imaging. Infarcts were detected in 90% of known cases of AIS evaluated in Yuen et al 14 . Moreover, pMRI can be utilized as a prognostic tool as ICH volumes, ischemic infarct volumes, and MLS identified on pMRI have been shown to correlate with patient outcome [12][13][14] .…”

Section: Present Deploymentsmentioning

confidence: 95%

Future of Neurology & Technology: Neuroimaging Made Accessible Using Low-Field, Portable MRI

et al. 2023

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In the 20thcentury, the advent of neuroimaging dramatically altered the field of neurological care. However, despite iterative advances since the invention of Computerized Tomography (CT) and Magnetic Resonance Imaging (MRI), little progress has been made to bring MR neuroimaging to the point-of-care. Recently, the emergence of a low-field (LF) (<1 Tesla (T)), portable MRI (pMRI) is setting the stage to revolutionize the landscape of accessible neuroimaging. Users can transport the pMRI into a variety of locations, using a standard 110-220V wall outlet. In this article, we discuss current applications for pMRI including in the acute and critical care settings, as well as the barriers to broad implementation, and future opportunities.BackgroundIn 1971, the development of CT transformed neuroimaging. For the first time, the brain could be visualized noninvasively1. MRI further revolutionized the field by coupling an external magnetic field with radiofrequency (RF) energy to provide greater soft tissue contrast and more precise anatomic visualization than CT2,3. However, conventional MRI (cMRI) scanners operate at a high magnetic field strength (1.5-3 T), are costly to purchase ($1M per T), and require expensive infrastructure4. Patients must be moved from clinical environments to controlled access remote imaging suites, often causing delays in image acquisition. This transport is associated with cardiovascular and respiratory risks, which are exacerbated by the inability to deploy interventions in transit5, 6. While use of portable CT (pCT) can evade these hazards, it carries the risk of radiation7. Thus, the ability to obtain MR neuroimaging at the point-of-care may reshape neurological care (Figure).For more information on pMRI background, please refer to eAppendix 1, which includes a discussion comparing pMRI versus cMRI and pCT and an explanation of pMRI specifications. eAppendix 1 also involves a clinical case highlighting pMRI’s utility and a description of similar devices.

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“…However, MRI accessibility is low and highly uneven around the world, with the majority of MRI scanners concentrated in high-income countries, leaving approximately 70% of the world’s population with little or no access to MRI 1 – 6 . Low-field MRI under 1 Telsa (T) has gained renewed interest 6 – 14 as a potential solution to this problem due to its significantly lower cost for purchase, installation, and maintenance compared to high-field MRI systems. In addition to the economic considerations, low-field MRI has a number of intrinsic advantages compared to high-field MRI, including improved patient comfort, low sensitivity to metallic implants, fewer image susceptibility artifacts, and extremely low radiofrequency specific absorption rate (SAR) 1 – 6 .…”

Section: Background and Summarymentioning

confidence: 99%

M4Raw: A multi-contrast, multi-repetition, multi-channel MRI k-space dataset for low-field MRI research

et al. 2023

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Recently, low-field magnetic resonance imaging (MRI) has gained renewed interest to promote MRI accessibility and affordability worldwide. The presented M4Raw dataset aims to facilitate methodology development and reproducible research in this field. The dataset comprises multi-channel brain k-space data collected from 183 healthy volunteers using a 0.3 Tesla whole-body MRI system, and includes T1-weighted, T2-weighted, and fluid attenuated inversion recovery (FLAIR) images with in-plane resolution of ~1.2 mm and through-plane resolution of 5 mm. Importantly, each contrast contains multiple repetitions, which can be used individually or to form multi-repetition averaged images. After excluding motion-corrupted data, the partitioned training and validation subsets contain 1024 and 240 volumes, respectively. To demonstrate the potential utility of this dataset, we trained deep learning models for image denoising and parallel imaging tasks and compared their performance with traditional reconstruction methods. This M4Raw dataset will be valuable for the development of advanced data-driven methods specifically for low-field MRI. It can also serve as a benchmark dataset for general MRI reconstruction algorithms.

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Portable, low-field magnetic resonance imaging enables highly accessible and dynamic bedside evaluation of ischemic stroke

Cited by 70 publications

References 63 publications

Validating Existing Scales for Identification of Acute Stroke in an Inpatient Setting

Validating Existing Scales for Identification of Acute Stroke in an Inpatient Setting

Future of Neurology & Technology: Neuroimaging Made Accessible Using Low-Field, Portable MRI

M4Raw: A multi-contrast, multi-repetition, multi-channel MRI k-space dataset for low-field MRI research

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