Point-of-Care Brain MRI: Preliminary Results from a Single-Center                     Retrospective Study

Kuoy, Edward; Glavis-Bloom, Justin; Hovis, Gabrielle; Yep, Brian; Biswas, Arabdha; Masudathaya, Lu-Aung Yosuke; Norrick, Lori A.; Limfueco, Julie; Soun, Jennifer E.; Chang, Peter D.; Chu, Eleanor; Akbari, Yama; Yaghmai, Vahid; Fox, J. Christian; Yu, Wengui; Chow, Daniel

doi:10.1148/radiol.211721

Cited by 18 publications

(20 citation statements)

References 13 publications

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“…For neurocritical care especially in remote and/or resource poor locations, pMRI has significant clinical utility, 1 – 12 although radiologists should be aware of the potential limitations of ULF MRI devices in terms of overall quality and take that into account when rendering diagnostic decisions. As an initial triage to aid in the care decision of whether to transport a patient to another location or have them remain on site, pMRI images likely provide enough information to render this decision.…”

Section: Discussionmentioning

confidence: 99%

“…Ultra-low-field (ULF) portable MRI (pMRI) systems (<100 mT) offer a potential solution for obtaining clinically useful images in challenging settings. Swoop ® (Hyperfine, Inc., Guilford, Connecticut, United States) is a Food and Drug Administration (FDA)-cleared, ULF (64 mT) portable, point-of-care MR system that has demonstrated clinical utility in a variety of clinical settings, such as intensive care units, 1 – 10 emergency departments, 4 , 8 and low-resource 11 and pediatric clinics 12 …”

Section: Introductionmentioning

confidence: 99%

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Image quality assessment of advanced reconstruction algorithm for point-of-care MRI scanner

Krupiński

Harris

Arlinghaus³

et al. 2023

J. Med. Imag.

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Portable magnetic resonance imaging (pMRI) has potential to rapidly acquire images at the patients' bedside to improve access in locations lacking MRI devices. The scanner under consideration has a magnetic field strength of 0.064 T, thus image-processing algorithms to improve image quality are required. Our study evaluated pMRI images produced using a deep learning (DL)-based advanced reconstruction scheme to improve image quality by reducing image blurring and noise to determine if diagnostic performance was similar to images acquired at 1.5 T.Approach: Six radiologists viewed 90 brain MRI cases (30 acute ischemic stroke (AIS), 30 hemorrhage, 30 no lesion) with T 1, T 2, and fluid attenuated inversion recovery sequences, once using standard of care (SOC) images (1.5 T) and once using pMRI DL-based advanced reconstruction images. Observers provided a diagnosis and decision confidence. Time to review each image was recorded.Results: Receiver operating characteristic area under the curve revealed overall no significant difference (p ¼ 0.0636) between pMRI and SOC images. Examining each abnormality, for acute ischemic stroke, there was a significant difference (p ¼ 0.0042) with SOC better than pMRI; but for hemorrhage, there was no significant difference (p ¼ 0.1950). There was no significant difference in viewing time for pMRI versus SOC (p ¼ 0.0766) or abnormality (p ¼ 0.3601). Conclusions:The deep learning (DL)-based reconstruction scheme to improve pMRI was successful for hemorrhage, but for acute ischemic stroke the scheme could still be improved. For neurocritical care especially in remote and/or resource poor locations, pMRI has significant clinical utility, although radiologists should be aware of limitations of low-field MRI devices in overall quality and take that into account when diagnosing. As an initial triage to aid in the decision of whether to transport or keep patients on site, pMRI images likely provide enough information.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Image quality assessment of advanced reconstruction algorithm for point-of-care MRI scanner

Krupiński

Harris

Arlinghaus³

et al. 2023

J. Med. Imag.

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“…The portability of low-and ultra-low-field systems makes them well suited to answer tractable and actionable macroscale questions -such as ventricle size, presence of hemorrhage, cerebral edema, and midline shift -in acute care environments. 84 Additionally, these systems may potentially offer general imaging where it was unavailable before in lower-resource and outpatient settings, with much more widespread dissemination and adoption anticipated. 71,85 Across all low-field strengths and system designs, imaging for screening, normative imaging, treatment monitoring, and serial follow-up imaging in patients are applications of great interest.…”

Section: Selection Of Appropriate Clinical Applicationsmentioning

confidence: 99%

“…Point‐of‐care settings of particular interest include the bedside, the ambulance, mobile MRI brought to a patient's home, mobile stroke units, and the battlefield. The portability of low‐ and ultra‐low‐field systems makes them well suited to answer tractable and actionable macroscale questions – such as ventricle size, presence of hemorrhage, cerebral edema, and midline shift – in acute care environments 84 . Additionally, these systems may potentially offer general imaging where it was unavailable before in lower‐resource and outpatient settings, with much more widespread dissemination and adoption anticipated 71,85 …”

Section: Key Themesmentioning

confidence: 99%

Low‐field MRI: A report on the 2022 ISMRM workshop

Campbell-Washburn

Keenan

et al. 2023

Magnetic Resonance in Med

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In March 2022, the first ISMRM Workshop on Low‐Field MRI was held virtually. The goals of this workshop were to discuss recent low field MRI technology including hardware and software developments, novel methodology, new contrast mechanisms, as well as the clinical translation and dissemination of these systems. The virtual Workshop was attended by 368 registrants from 24 countries, and included 34 invited talks, 100 abstract presentations, 2 panel discussions, and 2 live scanner demonstrations. Here, we report on the scientific content of the Workshop and identify the key themes that emerged. The subject matter of the Workshop reflected the ongoing developments of low‐field MRI as an accessible imaging modality that may expand the usage of MRI through cost reduction, portability, and ease of installation. Many talks in this Workshop addressed the use of computational power, efficient acquisitions, and contemporary hardware to overcome the SNR limitations associated with low field strength. Participants discussed the selection of appropriate clinical applications that leverage the unique capabilities of low‐field MRI within traditional radiology practices, other point‐of‐care settings, and the broader community. The notion of “image quality” versus “information content” was also discussed, as images from low‐field portable systems that are purpose‐built for clinical decision‐making may not replicate the current standard of clinical imaging. Speakers also described technical challenges and infrastructure challenges related to portability and widespread dissemination, and speculated about future directions for the field to improve the technology and establish clinical value.

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“…26 Magnetic Resonance Imaging (MRI) machines have also been developed for bedside operation and rapid analysis. 27 Even Hand Held Fundus Cameras based on smartphone cameras can assist doctors in retinal screening for diagnosing diseases like glaucoma and diabetes, 28 as shown in Figure 1. The portability of such devices enables their use in various settings, including homes, community clinics, and even remote areas, greatly expanding the coverage of medical services.…”

Section: Introductionmentioning

confidence: 99%

Efficient spine segmentation network based on multi‐scale feature extraction and multi‐dimensional spatial attention

Xu,

Wang,

et al. 2024

Int J Imaging Syst Tech

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In spine imaging, efficient automatic segmentation is crucial for clinical decision‐making, yet current models increase accuracy at the expense of elevated parameter counts and computational complexity, complicating integration with contemporary medical devices. Addressing identified challenges, this research introduces LE‐NeXt, a spine segmentation framework utilizing multi‐dimensional spatial attention and multi‐scale feature extraction, optimizing the architecture via convolution and MLP. It integrates lightweight convolutions and attention mechanisms within an encoder‐decoder model, enhancing stage‐specific feature extraction while ensuring efficiency. Experimental analyses on VerSe and SpineWeb datasets demonstrate that LE‐NeXt outperforms the lightweight U‐NeXt, enhancing IoU accuracy from 87.7 to 89.8 on VerSe, and exceeds the performance of established networks such as U‐Net and its variants. Significantly, on SpineWeb, LE‐NeXt not only surpasses Trans U‐Net in accuracy but also achieves a considerable reduction in both parameter count and computational complexity. These results emphasize LE‐NeXt's effectiveness in improving segmentation precision efficiently, optimally balancing computational efficiency and accuracy.

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Point-of-Care Brain MRI: Preliminary Results from a Single-Center Retrospective Study

Cited by 18 publications

References 13 publications

Image quality assessment of advanced reconstruction algorithm for point-of-care MRI scanner

Image quality assessment of advanced reconstruction algorithm for point-of-care MRI scanner

Low‐field MRI: A report on the 2022 ISMRM workshop

Efficient spine segmentation network based on multi‐scale feature extraction and multi‐dimensional spatial attention

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