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Balaji S, Wiley N, Poorman ME, Kolind SH. Low-field MRI for use in neurological diseases. Curr Opin Neurol 2024; 37:381-391. [PMID: 38813835 DOI: 10.1097/wco.0000000000001282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2024]
Abstract
PURPOSE OF REVIEW To review recent clinical uses of low-field magnetic resonance imaging (MRI) to guide incorporation into neurological practice. RECENT FINDINGS Use of low-field MRI has been demonstrated in applications including tumours, vascular pathologies, multiple sclerosis, brain injury, and paediatrics. Safety, workflow, and image quality have also been evaluated. SUMMARY Low-field MRI has the potential to increase access to critical brain imaging for patients who otherwise may not obtain imaging in a timely manner. This includes areas such as the intensive care unit and emergency room, where patients could be imaged at the point of care rather than be transported to the MRI scanner. Such systems are often more affordable than conventional systems, allowing them to be more easily deployed in resource constrained settings. A variety of systems are available on the market or in a research setting and are currently being used to determine clinical uses for these devices. The utility of such devices must be fully evaluated in clinical scenarios before adoption into standard practice can be achieved. This review summarizes recent clinical uses of low-field MR as well as safety, workflows, and image quality to aid practitioners in assessing this new technology.
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Affiliation(s)
- Sharada Balaji
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada
| | - Neale Wiley
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada
| | | | - Shannon H Kolind
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada
- Department of Medicine (Neurology)
- Department of Radiology
- International Collaboration on Repair Discoveries, Blusson Spinal Cord Centre, University of British Columbia, Vancouver, British Columbia, Canada
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Traboulsee A, Li DKB. Routine MR Imaging Protocol and Standardization in Central Nervous System Demyelinating Diseases. Neuroimaging Clin N Am 2024; 34:317-334. [PMID: 38942519 DOI: 10.1016/j.nic.2024.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2024]
Abstract
Standardized MR imaging protocols are important for the diagnosis and monitoring of patients with multiple sclerosis (MS) and the appropriate use of MR imaging in routine clinical practice. Advances in using MR imaging to establish an earlier diagnosis of MS, safety concerns regarding intravenous gadolinium-based contrast agents, and the value of spinal cord MR imaging for diagnostic, prognostic, and monitoring purposes suggest a changing role of MR imaging for the management and care of MS patients. The MR imaging protocol emphasizes 3 dimensional acquisitions for optimal comparison over time.
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Affiliation(s)
- Anthony Traboulsee
- Department of Medicine (Neurology), University of British Columbia, University of British Columbia Hospital, 2211 Wesbrook Mall, Room S113, Vancouver, British Columbia V6T 2B5, Canada.
| | - David K B Li
- Department of Radiology, University of British Columbia, University of British Columbia Hospital, 2211 Wesbrook Mall, Vancouver, British Columbia V6T 2B5, Canada
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Mašková B, Rožánek M, Gajdoš O, Karnoub E, Kamenský V, Donin G. Assessment of the Diagnostic Efficacy of Low-Field Magnetic Resonance Imaging: A Systematic Review. Diagnostics (Basel) 2024; 14:1564. [PMID: 39061702 PMCID: PMC11276230 DOI: 10.3390/diagnostics14141564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 07/04/2024] [Accepted: 07/11/2024] [Indexed: 07/28/2024] Open
Abstract
BACKGROUND In recent years, there has been an increasing effort to take advantage of the potential use of low magnetic induction devices with less than 1 T, referred to as Low-Field MRI (LF MRI). LF MRI systems were used, especially in the early days of magnetic resonance technology. Over time, magnetic induction values of 1.5 and 3 T have become the standard for clinical devices, mainly because LF MRI systems were suffering from significantly lower quality of the images, e.g., signal-noise ratio. In recent years, due to advances in image processing with artificial intelligence, there has been an increasing effort to take advantage of the potential use of LF MRI with induction of less than 1 T. This overview article focuses on the analysis of the evidence concerning the diagnostic efficacy of modern LF MRI systems and the clinical comparison of LF MRI with 1.5 T systems in imaging the nervous system, musculoskeletal system, and organs of the chest, abdomen, and pelvis. METHODOLOGY A systematic literature review of MEDLINE, PubMed, Scopus, Web of Science, and CENTRAL databases for the period 2018-2023 was performed according to the recommended PRISMA protocol. Data were analysed to identify studies comparing the accuracy, reliability and diagnostic performance of LF MRI technology compared to available 1.5 T MRI. RESULTS A total of 1275 publications were retrieved from the selected databases. Only two articles meeting all predefined inclusion criteria were selected for detailed assessment. CONCLUSIONS A limited number of robust studies on the accuracy and diagnostic performance of LF MRI compared with 1.5 T MRI was available. The current evidence is not sufficient to draw any definitive insights. More scientific research is needed to make informed conclusions regarding the effectiveness of LF MRI technology.
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Affiliation(s)
| | - Martin Rožánek
- Department of Biomedical Technology, Czech Technical University in Prague, 272 01 Kladno, Czech Republic; (B.M.); (O.G.); (E.K.); (V.K.); (G.D.)
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Altaf A, Shakir M, Irshad HA, Atif S, Kumari U, Islam O, Kimberly WT, Knopp E, Truwit C, Siddiqui K, Enam SA. Applications, limitations and advancements of ultra-low-field magnetic resonance imaging: A scoping review. Surg Neurol Int 2024; 15:218. [PMID: 38974534 PMCID: PMC11225429 DOI: 10.25259/sni_162_2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 05/17/2024] [Indexed: 07/09/2024] Open
Abstract
Background Ultra-low-field magnetic resonance imaging (ULF-MRI) has emerged as an alternative with several portable clinical applications. This review aims to comprehensively explore its applications, potential limitations, technological advancements, and expert recommendations. Methods A review of the literature was conducted across medical databases to identify relevant studies. Articles on clinical usage of ULF-MRI were included, and data regarding applications, limitations, and advancements were extracted. A total of 25 articles were included for qualitative analysis. Results The review reveals ULF-MRI efficacy in intensive care settings and intraoperatively. Technological strides are evident through innovative reconstruction techniques and integration with machine learning approaches. Additional advantages include features such as portability, cost-effectiveness, reduced power requirements, and improved patient comfort. However, alongside these strengths, certain limitations of ULF-MRI were identified, including low signal-to-noise ratio, limited resolution and length of scanning sequences, as well as variety and absence of regulatory-approved contrast-enhanced imaging. Recommendations from experts emphasize optimizing imaging quality, including addressing signal-to-noise ratio (SNR) and resolution, decreasing the length of scan time, and expanding point-of-care magnetic resonance imaging availability. Conclusion This review summarizes the potential of ULF-MRI. The technology's adaptability in intensive care unit settings and its diverse clinical and surgical applications, while accounting for SNR and resolution limitations, highlight its significance, especially in resource-limited settings. Technological advancements, alongside expert recommendations, pave the way for refining and expanding ULF-MRI's utility. However, adequate training is crucial for widespread utilization.
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Affiliation(s)
- Ahmed Altaf
- Department of Surgery, Section of Neurosurgery, Aga Khan University Hospital, Karachi, Sindh, Pakistan
| | - Muhammad Shakir
- Department of Surgery, Section of Neurosurgery, Aga Khan University Hospital, Karachi, Sindh, Pakistan
| | | | - Shiza Atif
- Medical College, Aga Khan University Hospital, Karachi, Sindh, Pakistan
| | - Usha Kumari
- Medical College, Peoples University of Medical and Health Sciences for Women, Karachi, Sindh, Pakistan
| | - Omar Islam
- Department of Diagnostic Radiology, Queen’s University, Kingston General Hospital, Kingston, Canada
| | - W. Taylor Kimberly
- Department of Neurology, Massachusetts General Hospital, Boston, United States
| | | | | | | | - S. Ather Enam
- Department of Surgery, Section of Neurosurgery, Aga Khan University Hospital, Karachi, Sindh, Pakistan
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5
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Donnay C, Okar SV, Tsagkas C, Gaitán MI, Poorman M, Reich DS, Nair G. Super resolution using sparse sampling at portable ultra-low field MR. Front Neurol 2024; 15:1330203. [PMID: 38854960 PMCID: PMC11157107 DOI: 10.3389/fneur.2024.1330203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 05/03/2024] [Indexed: 06/11/2024] Open
Abstract
Ultra-low field (ULF) magnetic resonance imaging (MRI) holds the potential to make MRI more accessible, given its cost-effectiveness, reduced power requirements, and portability. However, signal-to-noise ratio (SNR) drops with field strength, necessitating imaging with lower resolution and longer scan times. This study introduces a novel Fourier-based Super Resolution (FouSR) approach, designed to enhance the resolution of ULF MRI images with minimal increase in total scan time. FouSR combines spatial frequencies from two orthogonal ULF images of anisotropic resolution to create an isotropic T2-weighted fluid-attenuated inversion recovery (FLAIR) image. We hypothesized that FouSR could effectively recover information from under-sampled slice directions, thereby improving the delineation of multiple sclerosis (MS) lesions and other significant anatomical features. Importantly, the FouSR algorithm can be implemented on the scanner with changes to the k-space trajectory. Paired ULF (Hyperfine SWOOP, 0.064 tesla) and high field (Siemens, Skyra, 3 Tesla) FLAIR scans were collected on the same day from a phantom and a cohort of 10 participants with MS or suspected MS (6 female; mean ± SD age: 44.1 ± 4.1). ULF scans were acquired along both coronal and axial planes, featuring an in-plane resolution of 1.7 mm × 1.7 mm with a slice thickness of 5 mm. FouSR was evaluated against registered ULF coronal and axial scans, their average (ULF average) and a gold standard SR (ANTs SR). FouSR exhibited higher SNR (47.96 ± 12.6) compared to ULF coronal (36.7 ± 12.2) and higher lesion conspicuity (0.12 ± 0.06) compared to ULF axial (0.13 ± 0.07) but did not exhibit any significant differences contrast-to-noise-ratio (CNR) compared to other methods in patient scans. However, FouSR demonstrated superior image sharpness (0.025 ± 0.0040) compared to all other techniques (ULF coronal 0.021 ± 0.0037, q = 5.9, p-adj. = 0.011; ULF axial 0.018 ± 0.0026, q = 11.1, p-adj. = 0.0001; ULF average 0.019 ± 0.0034, q = 24.2, p-adj. < 0.0001) and higher lesion sharpness (-0.97 ± 0.31) when compared to the ULF average (-1.02 ± 0.37, t(543) = -10.174, p = <0.0001). Average blinded qualitative assessment by three experienced MS neurologists showed no significant difference in WML and sulci or gyri visualization between FouSR and other methods. FouSR can, in principle, be implemented on the scanner to produce clinically useful FLAIR images at higher resolution on the fly, providing a valuable tool for visualizing lesions and other anatomical structures in MS.
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Affiliation(s)
- Corinne Donnay
- Translational Neuroradiology Section, National Institutes of Health, National Institute of Neurological Disorders and Stroke, Bethesda, MD, United States
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Serhat V. Okar
- Translational Neuroradiology Section, National Institutes of Health, National Institute of Neurological Disorders and Stroke, Bethesda, MD, United States
| | - Charidimos Tsagkas
- Translational Neuroradiology Section, National Institutes of Health, National Institute of Neurological Disorders and Stroke, Bethesda, MD, United States
| | - María I. Gaitán
- Translational Neuroradiology Section, National Institutes of Health, National Institute of Neurological Disorders and Stroke, Bethesda, MD, United States
| | | | - Daniel S. Reich
- Translational Neuroradiology Section, National Institutes of Health, National Institute of Neurological Disorders and Stroke, Bethesda, MD, United States
| | - Govind Nair
- Quantitative MRI Core, National Institutes of Health, National Institute of Neurological Disorders and Stroke, Bethesda, MD, United States
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Zhao Y, Ding Y, Lau V, Man C, Su S, Xiao L, Leong ATL, Wu EX. Whole-body magnetic resonance imaging at 0.05 Tesla. Science 2024; 384:eadm7168. [PMID: 38723062 DOI: 10.1126/science.adm7168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 03/19/2024] [Indexed: 05/31/2024]
Abstract
Despite a half-century of advancements, global magnetic resonance imaging (MRI) accessibility remains limited and uneven, hindering its full potential in health care. Initially, MRI development focused on low fields around 0.05 Tesla, but progress halted after the introduction of the 1.5 Tesla whole-body superconducting scanner in 1983. Using a permanent 0.05 Tesla magnet and deep learning for electromagnetic interference elimination, we developed a whole-body scanner that operates using a standard wall power outlet and without radiofrequency and magnetic shielding. We demonstrated its wide-ranging applicability for imaging various anatomical structures. Furthermore, we developed three-dimensional deep learning reconstruction to boost image quality by harnessing extensive high-field MRI data. These advances pave the way for affordable deep learning-powered ultra-low-field MRI scanners, addressing unmet clinical needs in diverse health care settings worldwide.
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Affiliation(s)
- Yujiao Zhao
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Ye Ding
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Vick Lau
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Christopher Man
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Shi Su
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Linfang Xiao
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Alex T L Leong
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Ed X Wu
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
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Stankiewicz A, MacDonald D, Błasiak B, Kasparek A, Tomanek B, Cheng FY, Kruk D. 1H Spin-Lattice Relaxation Processes in Solutions of H 2N-Fe 3O 4 Nanoparticles: Insights from NMR Relaxometry. J Phys Chem B 2024; 128:2773-2781. [PMID: 38461422 DOI: 10.1021/acs.jpcb.3c07617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
1H spin-lattice relaxation experiments have been performed for water and glycerol/water solutions of H2N-Fe3O4 superparamagnetic nanoparticles (NPs) of about 7 nm diameter. The experiments encompass a broad frequency range covering 3 orders of magnitude, from 10 kHz to 10 MHz (referring to 1H resonance frequency), and have been performed in the temperature range from 298 to 313 K, varying the concentration of the superparamagnetic species. This extensive dataset has been used for twofold purposes. The first one is to serve as a challenge for thorough tests of theoretical models describing nuclear relaxation in solutions of superparamagnetic NPs, depending on their magnetic properties and dynamics of the solvent molecules. The challenge is posed by the wish to reproduce the data in a broad range of magnetic fields (not only at high fields) and by the need to explain the differences in the relaxation scenarios for water and glycerol/water solutions by varying only the solvent parameters. The second purpose is to get insights into the magnetic properties (electronic relaxation properties) of the nanoparticles due to their high applicational potential.
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Affiliation(s)
- Aleksandra Stankiewicz
- Department of Physics and Biophysics, University of Warmia and Mazury, Oczapowskiego 4, 10-719 Olsztyn, Poland
| | - David MacDonald
- Institute of Nuclear Physics, Polish Academy of Sciences, Radzikowskiego 152, 31-342 Kraków, Poland
| | - Barbara Błasiak
- Institute of Nuclear Physics, Polish Academy of Sciences, Radzikowskiego 152, 31-342 Kraków, Poland
| | - Adam Kasparek
- Department of Physics and Biophysics, University of Warmia and Mazury, Oczapowskiego 4, 10-719 Olsztyn, Poland
| | - Boguslaw Tomanek
- Institute of Nuclear Physics, Polish Academy of Sciences, Radzikowskiego 152, 31-342 Kraków, Poland
- Department of Oncology, Medical Physics Division, 3-12 University Terrace, 8303-112 St NW, Edmonton, Alberta T6G 2T4, Canada
| | - Fong-Yu Cheng
- Department of Chemistry, Chinese Culture University, 55, Hwa-Kang Road, Yang-Ming-Shan, Taipei 11114, Taiwan
| | - Danuta Kruk
- Department of Physics and Biophysics, University of Warmia and Mazury, Oczapowskiego 4, 10-719 Olsztyn, Poland
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Cooper R, Hayes RA, Corcoran M, Sheth KN, Arnold TC, Stein JM, Glahn DC, Jalbrzikowski M. Bridging the gap: improving correspondence between low-field and high-field magnetic resonance images in young people. Front Neurol 2024; 15:1339223. [PMID: 38585353 PMCID: PMC10995930 DOI: 10.3389/fneur.2024.1339223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 01/19/2024] [Indexed: 04/09/2024] Open
Abstract
Background Portable low-field-strength magnetic resonance imaging (MRI) systems represent a promising alternative to traditional high-field-strength systems with the potential to make MR technology available at scale in low-resource settings. However, lower image quality and resolution may limit the research and clinical potential of these devices. We tested two super-resolution methods to enhance image quality in a low-field MR system and compared their correspondence with images acquired from a high-field system in a sample of young people. Methods T1- and T2-weighted structural MR images were obtained from a low-field (64mT) Hyperfine and high-field (3T) Siemens system in N = 70 individuals (mean age = 20.39 years, range 9-26 years). We tested two super-resolution approaches to improve image correspondence between images acquired at high- and low-field: (1) processing via a convolutional neural network ('SynthSR'), and (2) multi-orientation image averaging. We extracted brain region volumes, cortical thickness, and cortical surface area estimates. We used Pearson correlations to test the correspondence between these measures, and Steiger Z tests to compare the difference in correspondence between standard imaging and super-resolution approaches. Results Single pairs of T1- and T2-weighted images acquired at low field showed high correspondence to high-field-strength images for estimates of total intracranial volume, surface area cortical volume, subcortical volume, and total brain volume (r range = 0.60-0.88). Correspondence was lower for cerebral white matter volume (r = 0.32, p = 0.007, q = 0.009) and non-significant for mean cortical thickness (r = -0.05, p = 0.664, q = 0.664). Processing images with SynthSR yielded significant improvements in correspondence for total brain volume, white matter volume, total surface area, subcortical volume, cortical volume, and total intracranial volume (r range = 0.85-0.97), with the exception of global mean cortical thickness (r = 0.14). An alternative multi-orientation image averaging approach improved correspondence for cerebral white matter and total brain volume. Processing with SynthSR also significantly improved correspondence across widespread regions for estimates of cortical volume, surface area and subcortical volume, as well as within isolated prefrontal and temporal regions for estimates of cortical thickness. Conclusion Applying super-resolution approaches to low-field imaging improves regional brain volume and surface area accuracy in young people. Finer-scale brain measurements, such as cortical thickness, remain challenging with the limited resolution of low-field systems.
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Affiliation(s)
- Rebecca Cooper
- Department of Psychiatry and Behavioral Sciences, Boston Children’s Hospital, Boston, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Rebecca A. Hayes
- Department of Psychiatry and Behavioral Sciences, Boston Children’s Hospital, Boston, MA, United States
| | - Mary Corcoran
- Department of Psychiatry and Behavioral Sciences, Boston Children’s Hospital, Boston, MA, United States
| | - Kevin N. Sheth
- Center for Brain and Mind Health, Yale School of Medicine, New Haven, CT, United States
| | - Thomas Campbell Arnold
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States
| | - Joel M. Stein
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - David C. Glahn
- Department of Psychiatry and Behavioral Sciences, Boston Children’s Hospital, Boston, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, United States
| | - Maria Jalbrzikowski
- Department of Psychiatry and Behavioral Sciences, Boston Children’s Hospital, Boston, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
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Lucas A, Campbell Arnold T, Okar SV, Vadali C, Kawatra KD, Ren Z, Cao Q, Shinohara RT, Schindler MK, Davis KA, Litt B, Reich DS, Stein JM. Multi-contrast high-field quality image synthesis for portable low-field MRI using generative adversarial networks and paired data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.28.23300409. [PMID: 38234785 PMCID: PMC10793526 DOI: 10.1101/2023.12.28.23300409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Introduction Portable low-field strength (64mT) MRI scanners promise to increase access to neuroimaging for clinical and research purposes, however these devices produce lower quality images compared to high-field scanners. In this study, we developed and evaluated a deep learning architecture to generate high-field quality brain images from low-field inputs using a paired dataset of multiple sclerosis (MS) patients scanned at 64mT and 3T. Methods A total of 49 MS patients were scanned on portable 64mT and standard 3T scanners at Penn (n=25) or the National Institutes of Health (NIH, n=24) with T1-weighted, T2-weighted and FLAIR acquisitions. Using this paired data, we developed a generative adversarial network (GAN) architecture for low- to high-field image translation (LowGAN). We then evaluated synthesized images with respect to image quality, brain morphometry, and white matter lesions. Results Synthetic high-field images demonstrated visually superior quality compared to low-field inputs and significantly higher normalized cross-correlation (NCC) to actual high-field images for T1 (p=0.001) and FLAIR (p<0.001) contrasts. LowGAN generally outperformed the current state-of-the-art for low-field volumetrics. For example, thalamic, lateral ventricle, and total cortical volumes in LowGAN outputs did not differ significantly from 3T measurements. Synthetic outputs preserved MS lesions and captured a known inverse relationship between total lesion volume and thalamic volume. Conclusions LowGAN generates synthetic high-field images with comparable visual and quantitative quality to actual high-field scans. Enhancing portable MRI image quality could add value and boost clinician confidence, enabling wider adoption of this technology.
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Affiliation(s)
- Alfredo Lucas
- Perelman School of Medicine, University of Pennsylvania
- Center for Neuroengineering and Therapeutics, Departments of Bioengineering and Neurology, University of Pennsylvania
| | - T Campbell Arnold
- Center for Neuroengineering and Therapeutics, Departments of Bioengineering and Neurology, University of Pennsylvania
| | - Serhat V Okar
- National Institute of Neurological Disorders and Stroke, National Institutes of Health
| | - Chetan Vadali
- Center for Neuroengineering and Therapeutics, Departments of Bioengineering and Neurology, University of Pennsylvania
- Department of Radiology, University of Pennsylvania
| | - Karan D Kawatra
- National Institute of Neurological Disorders and Stroke, National Institutes of Health
| | - Zheng Ren
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania
| | - Quy Cao
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania
| | - Matthew K Schindler
- Perelman School of Medicine, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
| | - Kathryn A Davis
- Perelman School of Medicine, University of Pennsylvania
- Center for Neuroengineering and Therapeutics, Departments of Bioengineering and Neurology, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
| | - Brian Litt
- Perelman School of Medicine, University of Pennsylvania
- Center for Neuroengineering and Therapeutics, Departments of Bioengineering and Neurology, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
| | - Daniel S Reich
- National Institute of Neurological Disorders and Stroke, National Institutes of Health
| | - Joel M Stein
- Perelman School of Medicine, University of Pennsylvania
- Center for Neuroengineering and Therapeutics, Departments of Bioengineering and Neurology, University of Pennsylvania
- Department of Radiology, University of Pennsylvania
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Jalloul M, Miranda-Schaeubinger M, Noor AM, Stein JM, Amiruddin R, Derbew HM, Mango VL, Akinola A, Hart K, Weygand J, Pollack E, Mohammed S, Scheel JR, Shell J, Dako F, Mhatre P, Kulinski L, Otero HJ, Mollura DJ. MRI scarcity in low- and middle-income countries. NMR IN BIOMEDICINE 2023; 36:e5022. [PMID: 37574441 DOI: 10.1002/nbm.5022] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 07/19/2023] [Accepted: 07/21/2023] [Indexed: 08/15/2023]
Abstract
Since the introduction of MRI as a sustainable diagnostic modality, global accessibility to its services has revealed a wide discrepancy between populations-leaving most of the population in LMICs without access to this important imaging modality. Several factors lead to the scarcity of MRI in LMICs; for example, inadequate infrastructure and the absence of a dedicated workforce are key factors in the scarcity observed. RAD-AID has contributed to the advancement of radiology globally by collaborating with our partners to make radiology more accessible for medically underserved communities. However, progress is slow and further investment is needed to ensure improved global access to MRI.
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Affiliation(s)
- Mohammad Jalloul
- Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | | | - Abass M Noor
- Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- RAD-AID International, Chevy Chase, Maryland, USA
- Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Joel M Stein
- Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Raisa Amiruddin
- Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Hermon Miliard Derbew
- Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- College of Health Science, Addis Ababa University, Addis Ababa, Ethiopia
| | - Victoria L Mango
- RAD-AID International, Chevy Chase, Maryland, USA
- Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | | | - Kelly Hart
- Tufts Medical Center, Boston, Massachusetts, USA
| | | | - Erica Pollack
- RAD-AID International, Chevy Chase, Maryland, USA
- University of Colorado Anschutz Medical Center, Aurora, Colorado, USA
| | - Sharon Mohammed
- RAD-AID International, Chevy Chase, Maryland, USA
- Bellevue Hospital Center NYCHHC, New York, New York, USA
| | - John R Scheel
- RAD-AID International, Chevy Chase, Maryland, USA
- Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jessica Shell
- RAD-AID International, Chevy Chase, Maryland, USA
- Siemens Medical Solutions USA, Inc., Cary, North Carolina, USA
| | - Farouk Dako
- RAD-AID International, Chevy Chase, Maryland, USA
- Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Pradnya Mhatre
- RAD-AID International, Chevy Chase, Maryland, USA
- Emory University School of Medicine, Atlanta, Georgia, USA
| | | | - Hansel J Otero
- Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
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11
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Islam KT, Zhong S, Zakavi P, Chen Z, Kavnoudias H, Farquharson S, Durbridge G, Barth M, McMahon KL, Parizel PM, Dwyer A, Egan GF, Law M, Chen Z. Improving portable low-field MRI image quality through image-to-image translation using paired low- and high-field images. Sci Rep 2023; 13:21183. [PMID: 38040835 PMCID: PMC10692211 DOI: 10.1038/s41598-023-48438-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 11/27/2023] [Indexed: 12/03/2023] Open
Abstract
Low-field portable magnetic resonance imaging (MRI) scanners are more accessible, cost-effective, sustainable with lower carbon emissions than superconducting high-field MRI scanners. However, the images produced have relatively poor image quality, lower signal-to-noise ratio, and limited spatial resolution. This study develops and investigates an image-to-image translation deep learning model, LoHiResGAN, to enhance the quality of low-field (64mT) MRI scans and generate synthetic high-field (3T) MRI scans. We employed a paired dataset comprising T1- and T2-weighted MRI sequences from the 64mT and 3T and compared the performance of the LoHiResGAN model with other state-of-the-art models, including GANs, CycleGAN, U-Net, and cGAN. Our proposed method demonstrates superior performance in terms of image quality metrics, such as normalized root-mean-squared error, structural similarity index measure, peak signal-to-noise ratio, and perception-based image quality evaluator. Additionally, we evaluated the accuracy of brain morphometry measurements for 33 brain regions across the original 3T, 64mT, and synthetic 3T images. The results indicate that the synthetic 3T images created using our proposed LoHiResGAN model significantly improve the image quality of low-field MRI data compared to other methods (GANs, CycleGAN, U-Net, cGAN) and provide more consistent brain morphometry measurements across various brain regions in reference to 3T. Synthetic images generated by our method demonstrated high quality both quantitatively and qualitatively. However, additional research, involving diverse datasets and clinical validation, is necessary to fully understand its applicability for clinical diagnostics, especially in settings where high-field MRI scanners are less accessible.
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Affiliation(s)
- Kh Tohidul Islam
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia.
| | - Shenjun Zhong
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
- Australian National Imaging Facility, Brisbane, QLD, Australia
| | - Parisa Zakavi
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
| | - Zhifeng Chen
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia
| | - Helen Kavnoudias
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Department of Radiology, Alfred Hospital, Melbourne, VIC, Australia
| | | | - Gail Durbridge
- Herston Imaging Research Facility, University of Queensland, Brisbane, QLD, Australia
| | - Markus Barth
- School of Information Technology and Electrical Engineering and Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia
| | - Katie L McMahon
- School of Clinical Science, Herston Imaging Research Facility, Queensland University of Technology, Brisbane, QLD, Australia
| | - Paul M Parizel
- David Hartley Chair of Radiology, Department of Radiology, Royal Perth Hospital, Perth, WA, Australia
- Medical School, University of Western Australia, Perth, WA, Australia
| | - Andrew Dwyer
- South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Gary F Egan
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
| | - Meng Law
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Department of Radiology, Alfred Hospital, Melbourne, VIC, Australia
| | - Zhaolin Chen
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia
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12
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Shoghli A, Chow D, Kuoy E, Yaghmai V. Current role of portable MRI in diagnosis of acute neurological conditions. Front Neurol 2023; 14:1255858. [PMID: 37840918 PMCID: PMC10576557 DOI: 10.3389/fneur.2023.1255858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 09/06/2023] [Indexed: 10/17/2023] Open
Abstract
Neuroimaging is an inevitable component of the assessment of neurological emergencies. Magnetic resonance imaging (MRI) is the preferred imaging modality for detecting neurological pathologies and provides higher sensitivity than other modalities. However, difficulties such as intra-hospital transport, long exam times, and availability in strict access-controlled suites limit its utility in emergency departments and intensive care units (ICUs). The evolution of novel imaging technologies over the past decades has led to the development of portable MRI (pMRI) machines that can be deployed at point-of-care. This article reviews pMRI technologies and their clinical implications in acute neurological conditions. Benefits of pMRI include timely and accurate detection of major acute neurological pathologies such as stroke and intracranial hemorrhage. Additionally, pMRI can be potentially used to monitor the progression of neurological complications by facilitating serial measurements at the bedside.
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Affiliation(s)
| | | | | | - Vahid Yaghmai
- Department of Radiological Sciences, School of Medicine, University of California, Irvine, Irvine, CA, United States
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13
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Kimberly WT, Sorby-Adams AJ, Webb AG, Wu EX, Beekman R, Bowry R, Schiff SJ, de Havenon A, Shen FX, Sze G, Schaefer P, Iglesias JE, Rosen MS, Sheth KN. Brain imaging with portable low-field MRI. NATURE REVIEWS BIOENGINEERING 2023; 1:617-630. [PMID: 37705717 PMCID: PMC10497072 DOI: 10.1038/s44222-023-00086-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/06/2023] [Indexed: 09/15/2023]
Abstract
The advent of portable, low-field MRI (LF-MRI) heralds new opportunities in neuroimaging. Low power requirements and transportability have enabled scanning outside the controlled environment of a conventional MRI suite, enhancing access to neuroimaging for indications that are not well suited to existing technologies. Maximizing the information extracted from the reduced signal-to-noise ratio of LF-MRI is crucial to developing clinically useful diagnostic images. Progress in electromagnetic noise cancellation and machine learning reconstruction algorithms from sparse k-space data as well as new approaches to image enhancement have now enabled these advancements. Coupling technological innovation with bedside imaging creates new prospects in visualizing the healthy brain and detecting acute and chronic pathological changes. Ongoing development of hardware, improvements in pulse sequences and image reconstruction, and validation of clinical utility will continue to accelerate this field. As further innovation occurs, portable LF-MRI will facilitate the democratization of MRI and create new applications not previously feasible with conventional systems.
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Affiliation(s)
- W Taylor Kimberly
- Department of Neurology and the Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Annabel J Sorby-Adams
- Department of Neurology and the Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrew G Webb
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Ed X Wu
- Laboratory of Biomedical Imaging and Signal Processing, Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Rachel Beekman
- Division of Neurocritical Care and Emergency Neurology, Department of Neurology, Yale New Haven Hospital and Yale School of Medicine, Yale Center for Brain & Mind Health, New Haven, CT, USA
| | - Ritvij Bowry
- Departments of Neurosurgery and Neurology, McGovern Medical School, University of Texas Health Neurosciences, Houston, TX, USA
| | - Steven J Schiff
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Adam de Havenon
- Division of Vascular Neurology, Department of Neurology, Yale New Haven Hospital and Yale School of Medicine, New Haven, CT, USA
| | - Francis X Shen
- Harvard Medical School Center for Bioethics, Harvard law School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Gordon Sze
- Department of Radiology, Yale New Haven Hospital and Yale School of Medicine, New Haven, CT, USA
| | - Pamela Schaefer
- Division of Neuroradiology, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Juan Eugenio Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Centre for Medical Image Computing, University College London, London, UK
- Computer Science and AI Laboratory, Massachusetts Institute of Technology, Boston, MA, USA
| | - Matthew S Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Kevin N Sheth
- Division of Neurocritical Care and Emergency Neurology, Department of Neurology, Yale New Haven Hospital and Yale School of Medicine, Yale Center for Brain & Mind Health, New Haven, CT, USA
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14
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Solomon AJ, Marrie RA, Viswanathan S, Correale J, Magyari M, Robertson NP, Saylor DR, Kaye W, Rechtman L, Bae E, Shinohara R, King R, Laurson-Doube J, Helme A. Global Barriers to the Diagnosis of Multiple Sclerosis: Data From the Multiple Sclerosis International Federation Atlas of MS, Third Edition. Neurology 2023; 101:e624-e635. [PMID: 37321866 PMCID: PMC10424832 DOI: 10.1212/wnl.0000000000207481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 04/18/2023] [Indexed: 06/17/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Recent data suggest increasing global prevalence of multiple sclerosis (MS). Early diagnosis of MS reduces the burden of disability-adjusted life years and associated health care costs. Yet diagnostic delays persist in MS care and even within national health care systems with robust resources, comprehensive registries, and MS subspecialist referral networks. The global prevalence and characteristics of barriers to expedited MS diagnosis, particularly in resource-restricted regions, have not been extensively studied. Recent revisions to MS diagnostic criteria demonstrate potential to facilitate earlier diagnosis, but global implementation remains largely unknown. METHODS The Multiple Sclerosis International Federation third edition of the Atlas of MS was a survey that assessed the current global state of diagnosis including adoption of MS diagnostic criteria; barriers to diagnosis with respect to the patient, health care provider, and health system; and existence of national guidelines or national standards for speed of MS diagnosis. RESULTS Coordinators from 107 countries (representing approximately 82% of the world population), participated. Eighty-three percent reported at least 1 "major barrier" to early MS diagnosis. The most frequently reported barriers included the following: "lack of awareness of MS symptoms among general public" (68%), "lack of awareness of MS symptoms among health care professionals" (59%), and "lack of availability of health care professionals with knowledge to diagnose MS" (44%). One-third reported lack of "specialist medical equipment or diagnostic tests." Thirty-four percent reported the use of only 2017 McDonald criteria (McD-C) for diagnosis, and 79% reported 2017 McD-C as the "most commonly used criteria." Sixty-six percent reported at least 1 barrier to the adoption of 2017 McD-C, including "neurologists lack awareness or training" by 45%. There was no significant association between national guidelines pertaining to MS diagnosis or practice standards addressing the speed of diagnosis and presence of barriers to early MS diagnosis and implementation of 2017 McD-C. DISCUSSION This study finds pervasive consistent global barriers to early diagnosis of MS. While these barriers reflected a lack of resources in many countries, data also suggest that interventions designed to develop and implement accessible education and training can provide cost-effective opportunities to improve access to early MS diagnosis.
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Affiliation(s)
- Andrew J Solomon
- From the Department of Neurological Sciences (A.J.S.), Larner College of Medicine at the University of Vermont, Burlington; Departments of Internal Medicine and Community Health Science (R.A.M.), Rady Faculty of Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, Canada; Department of Neurology (S.V.), Kuala Lumpur Hospital, Malaysia; Departamento de Neurologia (J.C.), Fleni, Buenos Aires; Institute of Biological Chemistry and Physical Chemistry (IQUIFIB) (J.C.), National Council for Scientific and Technical Research/University of Buenos Aires, Argentina; Department of Neurology (M.M.), Rigshospitalet, Copenhagen University Hospital, Denmark; Division of Psychological Medicine and Clinical Neuroscience (N.P.R.), Department of Neurology, Cardiff University, University Hospital of Wales, United Kingdom; Department of Neurology (D.R.S.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Internal Medicine (D.R.S.), University Teaching Hospital, Lusaka, Zambia; McKing Consulting Corporation (W.K., L.R.), Atlanta, GA; Department of Biostatistics, Epidemiology, and Informatics (E.B., R.S.), Perelman School of Medicine, University of Pennsylvania, Philadelphia; and Multiple Sclerosis International Federation (R.K., J.L.-D., A.H.), London, United Kingdom.
| | - Ruth Ann Marrie
- From the Department of Neurological Sciences (A.J.S.), Larner College of Medicine at the University of Vermont, Burlington; Departments of Internal Medicine and Community Health Science (R.A.M.), Rady Faculty of Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, Canada; Department of Neurology (S.V.), Kuala Lumpur Hospital, Malaysia; Departamento de Neurologia (J.C.), Fleni, Buenos Aires; Institute of Biological Chemistry and Physical Chemistry (IQUIFIB) (J.C.), National Council for Scientific and Technical Research/University of Buenos Aires, Argentina; Department of Neurology (M.M.), Rigshospitalet, Copenhagen University Hospital, Denmark; Division of Psychological Medicine and Clinical Neuroscience (N.P.R.), Department of Neurology, Cardiff University, University Hospital of Wales, United Kingdom; Department of Neurology (D.R.S.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Internal Medicine (D.R.S.), University Teaching Hospital, Lusaka, Zambia; McKing Consulting Corporation (W.K., L.R.), Atlanta, GA; Department of Biostatistics, Epidemiology, and Informatics (E.B., R.S.), Perelman School of Medicine, University of Pennsylvania, Philadelphia; and Multiple Sclerosis International Federation (R.K., J.L.-D., A.H.), London, United Kingdom
| | - Shanthi Viswanathan
- From the Department of Neurological Sciences (A.J.S.), Larner College of Medicine at the University of Vermont, Burlington; Departments of Internal Medicine and Community Health Science (R.A.M.), Rady Faculty of Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, Canada; Department of Neurology (S.V.), Kuala Lumpur Hospital, Malaysia; Departamento de Neurologia (J.C.), Fleni, Buenos Aires; Institute of Biological Chemistry and Physical Chemistry (IQUIFIB) (J.C.), National Council for Scientific and Technical Research/University of Buenos Aires, Argentina; Department of Neurology (M.M.), Rigshospitalet, Copenhagen University Hospital, Denmark; Division of Psychological Medicine and Clinical Neuroscience (N.P.R.), Department of Neurology, Cardiff University, University Hospital of Wales, United Kingdom; Department of Neurology (D.R.S.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Internal Medicine (D.R.S.), University Teaching Hospital, Lusaka, Zambia; McKing Consulting Corporation (W.K., L.R.), Atlanta, GA; Department of Biostatistics, Epidemiology, and Informatics (E.B., R.S.), Perelman School of Medicine, University of Pennsylvania, Philadelphia; and Multiple Sclerosis International Federation (R.K., J.L.-D., A.H.), London, United Kingdom
| | - Jorge Correale
- From the Department of Neurological Sciences (A.J.S.), Larner College of Medicine at the University of Vermont, Burlington; Departments of Internal Medicine and Community Health Science (R.A.M.), Rady Faculty of Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, Canada; Department of Neurology (S.V.), Kuala Lumpur Hospital, Malaysia; Departamento de Neurologia (J.C.), Fleni, Buenos Aires; Institute of Biological Chemistry and Physical Chemistry (IQUIFIB) (J.C.), National Council for Scientific and Technical Research/University of Buenos Aires, Argentina; Department of Neurology (M.M.), Rigshospitalet, Copenhagen University Hospital, Denmark; Division of Psychological Medicine and Clinical Neuroscience (N.P.R.), Department of Neurology, Cardiff University, University Hospital of Wales, United Kingdom; Department of Neurology (D.R.S.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Internal Medicine (D.R.S.), University Teaching Hospital, Lusaka, Zambia; McKing Consulting Corporation (W.K., L.R.), Atlanta, GA; Department of Biostatistics, Epidemiology, and Informatics (E.B., R.S.), Perelman School of Medicine, University of Pennsylvania, Philadelphia; and Multiple Sclerosis International Federation (R.K., J.L.-D., A.H.), London, United Kingdom
| | - Melinda Magyari
- From the Department of Neurological Sciences (A.J.S.), Larner College of Medicine at the University of Vermont, Burlington; Departments of Internal Medicine and Community Health Science (R.A.M.), Rady Faculty of Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, Canada; Department of Neurology (S.V.), Kuala Lumpur Hospital, Malaysia; Departamento de Neurologia (J.C.), Fleni, Buenos Aires; Institute of Biological Chemistry and Physical Chemistry (IQUIFIB) (J.C.), National Council for Scientific and Technical Research/University of Buenos Aires, Argentina; Department of Neurology (M.M.), Rigshospitalet, Copenhagen University Hospital, Denmark; Division of Psychological Medicine and Clinical Neuroscience (N.P.R.), Department of Neurology, Cardiff University, University Hospital of Wales, United Kingdom; Department of Neurology (D.R.S.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Internal Medicine (D.R.S.), University Teaching Hospital, Lusaka, Zambia; McKing Consulting Corporation (W.K., L.R.), Atlanta, GA; Department of Biostatistics, Epidemiology, and Informatics (E.B., R.S.), Perelman School of Medicine, University of Pennsylvania, Philadelphia; and Multiple Sclerosis International Federation (R.K., J.L.-D., A.H.), London, United Kingdom
| | - Neil P Robertson
- From the Department of Neurological Sciences (A.J.S.), Larner College of Medicine at the University of Vermont, Burlington; Departments of Internal Medicine and Community Health Science (R.A.M.), Rady Faculty of Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, Canada; Department of Neurology (S.V.), Kuala Lumpur Hospital, Malaysia; Departamento de Neurologia (J.C.), Fleni, Buenos Aires; Institute of Biological Chemistry and Physical Chemistry (IQUIFIB) (J.C.), National Council for Scientific and Technical Research/University of Buenos Aires, Argentina; Department of Neurology (M.M.), Rigshospitalet, Copenhagen University Hospital, Denmark; Division of Psychological Medicine and Clinical Neuroscience (N.P.R.), Department of Neurology, Cardiff University, University Hospital of Wales, United Kingdom; Department of Neurology (D.R.S.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Internal Medicine (D.R.S.), University Teaching Hospital, Lusaka, Zambia; McKing Consulting Corporation (W.K., L.R.), Atlanta, GA; Department of Biostatistics, Epidemiology, and Informatics (E.B., R.S.), Perelman School of Medicine, University of Pennsylvania, Philadelphia; and Multiple Sclerosis International Federation (R.K., J.L.-D., A.H.), London, United Kingdom
| | - Deanna R Saylor
- From the Department of Neurological Sciences (A.J.S.), Larner College of Medicine at the University of Vermont, Burlington; Departments of Internal Medicine and Community Health Science (R.A.M.), Rady Faculty of Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, Canada; Department of Neurology (S.V.), Kuala Lumpur Hospital, Malaysia; Departamento de Neurologia (J.C.), Fleni, Buenos Aires; Institute of Biological Chemistry and Physical Chemistry (IQUIFIB) (J.C.), National Council for Scientific and Technical Research/University of Buenos Aires, Argentina; Department of Neurology (M.M.), Rigshospitalet, Copenhagen University Hospital, Denmark; Division of Psychological Medicine and Clinical Neuroscience (N.P.R.), Department of Neurology, Cardiff University, University Hospital of Wales, United Kingdom; Department of Neurology (D.R.S.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Internal Medicine (D.R.S.), University Teaching Hospital, Lusaka, Zambia; McKing Consulting Corporation (W.K., L.R.), Atlanta, GA; Department of Biostatistics, Epidemiology, and Informatics (E.B., R.S.), Perelman School of Medicine, University of Pennsylvania, Philadelphia; and Multiple Sclerosis International Federation (R.K., J.L.-D., A.H.), London, United Kingdom
| | - Wendy Kaye
- From the Department of Neurological Sciences (A.J.S.), Larner College of Medicine at the University of Vermont, Burlington; Departments of Internal Medicine and Community Health Science (R.A.M.), Rady Faculty of Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, Canada; Department of Neurology (S.V.), Kuala Lumpur Hospital, Malaysia; Departamento de Neurologia (J.C.), Fleni, Buenos Aires; Institute of Biological Chemistry and Physical Chemistry (IQUIFIB) (J.C.), National Council for Scientific and Technical Research/University of Buenos Aires, Argentina; Department of Neurology (M.M.), Rigshospitalet, Copenhagen University Hospital, Denmark; Division of Psychological Medicine and Clinical Neuroscience (N.P.R.), Department of Neurology, Cardiff University, University Hospital of Wales, United Kingdom; Department of Neurology (D.R.S.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Internal Medicine (D.R.S.), University Teaching Hospital, Lusaka, Zambia; McKing Consulting Corporation (W.K., L.R.), Atlanta, GA; Department of Biostatistics, Epidemiology, and Informatics (E.B., R.S.), Perelman School of Medicine, University of Pennsylvania, Philadelphia; and Multiple Sclerosis International Federation (R.K., J.L.-D., A.H.), London, United Kingdom
| | - Lindsay Rechtman
- From the Department of Neurological Sciences (A.J.S.), Larner College of Medicine at the University of Vermont, Burlington; Departments of Internal Medicine and Community Health Science (R.A.M.), Rady Faculty of Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, Canada; Department of Neurology (S.V.), Kuala Lumpur Hospital, Malaysia; Departamento de Neurologia (J.C.), Fleni, Buenos Aires; Institute of Biological Chemistry and Physical Chemistry (IQUIFIB) (J.C.), National Council for Scientific and Technical Research/University of Buenos Aires, Argentina; Department of Neurology (M.M.), Rigshospitalet, Copenhagen University Hospital, Denmark; Division of Psychological Medicine and Clinical Neuroscience (N.P.R.), Department of Neurology, Cardiff University, University Hospital of Wales, United Kingdom; Department of Neurology (D.R.S.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Internal Medicine (D.R.S.), University Teaching Hospital, Lusaka, Zambia; McKing Consulting Corporation (W.K., L.R.), Atlanta, GA; Department of Biostatistics, Epidemiology, and Informatics (E.B., R.S.), Perelman School of Medicine, University of Pennsylvania, Philadelphia; and Multiple Sclerosis International Federation (R.K., J.L.-D., A.H.), London, United Kingdom
| | - Eunchan Bae
- From the Department of Neurological Sciences (A.J.S.), Larner College of Medicine at the University of Vermont, Burlington; Departments of Internal Medicine and Community Health Science (R.A.M.), Rady Faculty of Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, Canada; Department of Neurology (S.V.), Kuala Lumpur Hospital, Malaysia; Departamento de Neurologia (J.C.), Fleni, Buenos Aires; Institute of Biological Chemistry and Physical Chemistry (IQUIFIB) (J.C.), National Council for Scientific and Technical Research/University of Buenos Aires, Argentina; Department of Neurology (M.M.), Rigshospitalet, Copenhagen University Hospital, Denmark; Division of Psychological Medicine and Clinical Neuroscience (N.P.R.), Department of Neurology, Cardiff University, University Hospital of Wales, United Kingdom; Department of Neurology (D.R.S.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Internal Medicine (D.R.S.), University Teaching Hospital, Lusaka, Zambia; McKing Consulting Corporation (W.K., L.R.), Atlanta, GA; Department of Biostatistics, Epidemiology, and Informatics (E.B., R.S.), Perelman School of Medicine, University of Pennsylvania, Philadelphia; and Multiple Sclerosis International Federation (R.K., J.L.-D., A.H.), London, United Kingdom
| | - Russell Shinohara
- From the Department of Neurological Sciences (A.J.S.), Larner College of Medicine at the University of Vermont, Burlington; Departments of Internal Medicine and Community Health Science (R.A.M.), Rady Faculty of Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, Canada; Department of Neurology (S.V.), Kuala Lumpur Hospital, Malaysia; Departamento de Neurologia (J.C.), Fleni, Buenos Aires; Institute of Biological Chemistry and Physical Chemistry (IQUIFIB) (J.C.), National Council for Scientific and Technical Research/University of Buenos Aires, Argentina; Department of Neurology (M.M.), Rigshospitalet, Copenhagen University Hospital, Denmark; Division of Psychological Medicine and Clinical Neuroscience (N.P.R.), Department of Neurology, Cardiff University, University Hospital of Wales, United Kingdom; Department of Neurology (D.R.S.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Internal Medicine (D.R.S.), University Teaching Hospital, Lusaka, Zambia; McKing Consulting Corporation (W.K., L.R.), Atlanta, GA; Department of Biostatistics, Epidemiology, and Informatics (E.B., R.S.), Perelman School of Medicine, University of Pennsylvania, Philadelphia; and Multiple Sclerosis International Federation (R.K., J.L.-D., A.H.), London, United Kingdom
| | - Rachel King
- From the Department of Neurological Sciences (A.J.S.), Larner College of Medicine at the University of Vermont, Burlington; Departments of Internal Medicine and Community Health Science (R.A.M.), Rady Faculty of Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, Canada; Department of Neurology (S.V.), Kuala Lumpur Hospital, Malaysia; Departamento de Neurologia (J.C.), Fleni, Buenos Aires; Institute of Biological Chemistry and Physical Chemistry (IQUIFIB) (J.C.), National Council for Scientific and Technical Research/University of Buenos Aires, Argentina; Department of Neurology (M.M.), Rigshospitalet, Copenhagen University Hospital, Denmark; Division of Psychological Medicine and Clinical Neuroscience (N.P.R.), Department of Neurology, Cardiff University, University Hospital of Wales, United Kingdom; Department of Neurology (D.R.S.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Internal Medicine (D.R.S.), University Teaching Hospital, Lusaka, Zambia; McKing Consulting Corporation (W.K., L.R.), Atlanta, GA; Department of Biostatistics, Epidemiology, and Informatics (E.B., R.S.), Perelman School of Medicine, University of Pennsylvania, Philadelphia; and Multiple Sclerosis International Federation (R.K., J.L.-D., A.H.), London, United Kingdom
| | - Joanna Laurson-Doube
- From the Department of Neurological Sciences (A.J.S.), Larner College of Medicine at the University of Vermont, Burlington; Departments of Internal Medicine and Community Health Science (R.A.M.), Rady Faculty of Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, Canada; Department of Neurology (S.V.), Kuala Lumpur Hospital, Malaysia; Departamento de Neurologia (J.C.), Fleni, Buenos Aires; Institute of Biological Chemistry and Physical Chemistry (IQUIFIB) (J.C.), National Council for Scientific and Technical Research/University of Buenos Aires, Argentina; Department of Neurology (M.M.), Rigshospitalet, Copenhagen University Hospital, Denmark; Division of Psychological Medicine and Clinical Neuroscience (N.P.R.), Department of Neurology, Cardiff University, University Hospital of Wales, United Kingdom; Department of Neurology (D.R.S.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Internal Medicine (D.R.S.), University Teaching Hospital, Lusaka, Zambia; McKing Consulting Corporation (W.K., L.R.), Atlanta, GA; Department of Biostatistics, Epidemiology, and Informatics (E.B., R.S.), Perelman School of Medicine, University of Pennsylvania, Philadelphia; and Multiple Sclerosis International Federation (R.K., J.L.-D., A.H.), London, United Kingdom
| | - Anne Helme
- From the Department of Neurological Sciences (A.J.S.), Larner College of Medicine at the University of Vermont, Burlington; Departments of Internal Medicine and Community Health Science (R.A.M.), Rady Faculty of Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, Canada; Department of Neurology (S.V.), Kuala Lumpur Hospital, Malaysia; Departamento de Neurologia (J.C.), Fleni, Buenos Aires; Institute of Biological Chemistry and Physical Chemistry (IQUIFIB) (J.C.), National Council for Scientific and Technical Research/University of Buenos Aires, Argentina; Department of Neurology (M.M.), Rigshospitalet, Copenhagen University Hospital, Denmark; Division of Psychological Medicine and Clinical Neuroscience (N.P.R.), Department of Neurology, Cardiff University, University Hospital of Wales, United Kingdom; Department of Neurology (D.R.S.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Internal Medicine (D.R.S.), University Teaching Hospital, Lusaka, Zambia; McKing Consulting Corporation (W.K., L.R.), Atlanta, GA; Department of Biostatistics, Epidemiology, and Informatics (E.B., R.S.), Perelman School of Medicine, University of Pennsylvania, Philadelphia; and Multiple Sclerosis International Federation (R.K., J.L.-D., A.H.), London, United Kingdom
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15
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Nathoo N, Zeydan B, Neyal N, Chelf C, Okuda DT, Kantarci OH. Do magnetic resonance imaging features differ between persons with multiple sclerosis of various races and ethnicities? Front Neurol 2023; 14:1215774. [PMID: 37448745 PMCID: PMC10338060 DOI: 10.3389/fneur.2023.1215774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 06/12/2023] [Indexed: 07/15/2023] Open
Abstract
Those of African American or Latin American descent have been demonstrated to have more severe clinical presentations of multiple sclerosis (MS) than non-Latin American White people with MS. Concurrently, radiological burden of disease on magnetic resonance imaging (MRI) in African Americans with MS has also been described as being more aggressive. Here, we review MRI studies in diverse racial and ethnic groups (adult and pediatric) investigating lesion burden, inflammation, neurodegeneration, and imaging response to disease modifying therapy. We also discuss why such disparities may exist beyond biology, and how future studies may provide greater insights into underlying differences.
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Affiliation(s)
- Nabeela Nathoo
- Division of Multiple Sclerosis and Autoimmune Neurology, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Burcu Zeydan
- Division of Multiple Sclerosis and Autoimmune Neurology, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Nur Neyal
- Division of Multiple Sclerosis and Autoimmune Neurology, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Cynthia Chelf
- Mayo Clinic College of Medicine and Science, Library-Public Services, Mayo Clinic, Rochester, MN, United States
| | - Darin T. Okuda
- Department of Neurology, The University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Orhun H. Kantarci
- Division of Multiple Sclerosis and Autoimmune Neurology, Department of Neurology, Mayo Clinic, Rochester, MN, United States
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16
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de Havenon A, Parasuram NR, Crawford AL, Mazurek MH, Chavva IR, Yadlapalli V, Iglesias JE, Rosen MS, Falcone GJ, Payabvash S, Sze G, Sharma R, Schiff SJ, Safdar B, Wira C, Kimberly WT, Sheth KN. Identification of White Matter Hyperintensities in Routine Emergency Department Visits Using Portable Bedside Magnetic Resonance Imaging. J Am Heart Assoc 2023; 12:e029242. [PMID: 37218590 PMCID: PMC10381997 DOI: 10.1161/jaha.122.029242] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 03/27/2023] [Indexed: 05/24/2023]
Abstract
Background White matter hyperintensity (WMH) on magnetic resonance imaging (MRI) of the brain is associated with vascular cognitive impairment, cardiovascular disease, and stroke. We hypothesized that portable magnetic resonance imaging (pMRI) could successfully identify WMHs and facilitate doing so in an unconventional setting. Methods and Results In a retrospective cohort of patients with both a conventional 1.5 Tesla MRI and pMRI, we report Cohen's kappa (κ) to measure agreement for detection of moderate to severe WMH (Fazekas ≥2). In a subsequent prospective observational study, we enrolled adult patients with a vascular risk factor being evaluated in the emergency department for a nonstroke complaint and measured WMH using pMRI. In the retrospective cohort, we included 33 patients, identifying 16 (49.5%) with WMH on conventional MRI. Between 2 raters evaluating pMRI, the interrater agreement on WMH was strong (κ=0.81), and between 1 rater for conventional MRI and the 2 raters for pMRI, intermodality agreement was moderate (κ=0.66, 0.60). In the prospective cohort we enrolled 91 individuals (mean age, 62.6 years; 53.9% men; 73.6% with hypertension), of which 58.2% had WMHs on pMRI. Among 37 Black and Hispanic individuals, the Area Deprivation Index was higher (versus White, 51.8±12.9 versus 37.9±11.9; P<0.001). Among 81 individuals who did not have a standard-of-care MRI in the preceding year, we identified WMHs in 43 of 81 (53.1%). Conclusions Portable, low-field imaging could be useful for identifying moderate to severe WMHs. These preliminary results introduce a novel role for pMRI outside of acute care and the potential role for pMRI to reduce disparities in neuroimaging.
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Affiliation(s)
- Adam de Havenon
- Department of NeurologyYale University School of MedicineNew HavenCTUSA
- Center for Brain and Mind HealthYale University School of MedicineNew HavenCTUSA
| | | | - Anna L. Crawford
- Department of NeurologyYale University School of MedicineNew HavenCTUSA
| | - Mercy H. Mazurek
- Department of NeurologyYale University School of MedicineNew HavenCTUSA
| | - Isha R. Chavva
- Department of NeurologyYale University School of MedicineNew HavenCTUSA
| | | | - Juan E. Iglesias
- Department of Neurology, Division of Neurocritical CareMassachusetts General HospitalBostonMAUSA
- Computer Science and Artificial Intelligence LabMassachusetts Institute of TechnologyCambridgeMAUSA
- Center for Biomedical ImagingMassachusetts General Hospital and Harvard Medical SchoolDepartment of Physics, Harvard UniversityBostonMAUSA
| | - Matthew S. Rosen
- Department of Neurology, Division of Neurocritical CareMassachusetts General HospitalBostonMAUSA
| | - Guido J. Falcone
- Department of NeurologyYale University School of MedicineNew HavenCTUSA
| | - Seyedmehdi Payabvash
- Center for Brain and Mind HealthYale University School of MedicineNew HavenCTUSA
- Department of RadiologyYale University School of MedicineNew HavenCOUSA
| | - Gordon Sze
- Department of RadiologyYale University School of MedicineNew HavenCOUSA
| | - Richa Sharma
- Department of NeurologyYale University School of MedicineNew HavenCTUSA
- Center for Brain and Mind HealthYale University School of MedicineNew HavenCTUSA
| | - Steven J. Schiff
- Department of NeurosurgeryYale University School of MedicineNew HavenCOUSA
| | - Basmah Safdar
- Department of Emergency MedicineYale University School of MedicineNew HavenCOUSA
| | - Charles Wira
- Department of Emergency MedicineYale University School of MedicineNew HavenCOUSA
| | - William T. Kimberly
- Department of Neurology, Division of Neurocritical CareMassachusetts General HospitalBostonMAUSA
| | - Kevin N. Sheth
- Department of NeurologyYale University School of MedicineNew HavenCTUSA
- Center for Brain and Mind HealthYale University School of MedicineNew HavenCTUSA
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17
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Ertl-Wagner B, Wagner M. Ultralow-Field-Strength MRI and Artificial Intelligence: How Low Can We Go and How High Can We Reach? Radiology 2023; 306:e222302. [PMID: 36346316 DOI: 10.1148/radiol.222302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Birgit Ertl-Wagner
- From the Department of Medical Imaging, University of Toronto, 555 University Ave, Toronto, ON, Canada M5S 1A1
| | - Matthias Wagner
- From the Department of Medical Imaging, University of Toronto, 555 University Ave, Toronto, ON, Canada M5S 1A1
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18
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Deoni SCL, Burton P, Beauchemin J, Cano-Lorente R, De Both MD, Johnson M, Ryan L, Huentelman MJ. Neuroimaging and verbal memory assessment in healthy aging adults using a portable low-field MRI scanner and a web-based platform: results from a proof-of-concept population-based cross-section study. Brain Struct Funct 2023; 228:493-509. [PMID: 36352153 PMCID: PMC9646260 DOI: 10.1007/s00429-022-02595-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 11/01/2022] [Indexed: 11/10/2022]
Abstract
Consumer wearables and health monitors, internet-based health and cognitive assessments, and at-home biosample (e.g., saliva and capillary blood) collection kits are increasingly used by public health researchers for large population-based studies without requiring intensive in-person visits. Alongside reduced participant time burden, remote and virtual data collection allows the participation of individuals who live long distances from hospital or university research centers, or who lack access to transportation. Unfortunately, studies that include magnetic resonance neuroimaging are challenging to perform remotely given the infrastructure requirements of MRI scanners, and, as a result, they often omit socially, economically, and educationally disadvantaged individuals. Lower field strength systems (< 100 mT) offer the potential to perform neuroimaging at a participant's home, enabling more accessible and equitable research. Here we report the first use of a low-field MRI "scan van" with an online assessment of paired-associate learning (PAL) to examine associations between brain morphometry and verbal memory performance. In a sample of 67 individuals, 18-93 years of age, imaged at or near their home, we show expected white and gray matter volume trends with age and find significant (p < 0.05 FWE) associations between PAL performance and hippocampus, amygdala, caudate, and thalamic volumes. High-quality data were acquired in 93% of individuals, and at-home scanning was preferred by all individuals with prior MRI at a hospital or research setting. Results demonstrate the feasibility of remote neuroimaging and cognitive data collection, with important implications for engaging traditionally under-represented communities in neuroimaging research.
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Affiliation(s)
- Sean C L Deoni
- Maternal, Newborn, and Child Health Discovery & Tools, Bill & Melinda Gates Foundation, 500 5th Ave, Seattle, WA, 98109, USA.
| | - Phoebe Burton
- Advanced Baby Imaging Lab, Rhode Island Hospital, Providence, RI, USA
- Department of Pediatrics, Warren Alpert Medical School at Brown University, Providence, RI, USA
| | - Jennifer Beauchemin
- Advanced Baby Imaging Lab, Rhode Island Hospital, Providence, RI, USA
- Department of Pediatrics, Warren Alpert Medical School at Brown University, Providence, RI, USA
| | - Rosa Cano-Lorente
- Advanced Baby Imaging Lab, Rhode Island Hospital, Providence, RI, USA
- Department of Pediatrics, Warren Alpert Medical School at Brown University, Providence, RI, USA
| | | | | | - Lee Ryan
- Department of Psychology, University of Arizona, Tucson, AZ, USA
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19
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Ditkofsky N, Lin AW, Mathur S, Bharatha A. Point-of-Care MRI Shows Great Promise. Radiology 2023; 307:e222071. [PMID: 36809219 DOI: 10.1148/radiol.222071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Affiliation(s)
- Noah Ditkofsky
- Division of Emergency, Trauma and Acute Care Radiology Department of Medical Imaging, St. Michael's Hospital, Unity Health Toronto, University of Toronto
| | - Amy W Lin
- Division of Neuroradiology, Department of Medical Imaging, St. Michael's Hospital, Unity Health Toronto, University of Toronto
| | - Shobhit Mathur
- Division of Emergency, Trauma and Acute Care Radiology Department of Medical Imaging, St. Michael's Hospital, Unity Health Toronto, University of Toronto.,Division of Neuroradiology, Department of Medical Imaging, St. Michael's Hospital, Unity Health Toronto, University of Toronto.,Medical Imaging Office of Research and Innovation, Li Ka Shing Knowledge Institute, 30 Bond St, Toronto, ON, Canada M5B 1W8
| | - Aditya Bharatha
- Division of Neuroradiology, Department of Medical Imaging, St. Michael's Hospital, Unity Health Toronto, University of Toronto.,Medical Imaging Office of Research and Innovation, Li Ka Shing Knowledge Institute, 30 Bond St, Toronto, ON, Canada M5B 1W8
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20
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Arnold TC, Freeman CW, Litt B, Stein JM. Low-field MRI: Clinical promise and challenges. J Magn Reson Imaging 2023; 57:25-44. [PMID: 36120962 PMCID: PMC9771987 DOI: 10.1002/jmri.28408] [Citation(s) in RCA: 62] [Impact Index Per Article: 62.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 08/11/2022] [Accepted: 08/12/2022] [Indexed: 02/03/2023] Open
Abstract
Modern MRI scanners have trended toward higher field strengths to maximize signal and resolution while minimizing scan time. However, high-field devices remain expensive to install and operate, making them scarce outside of high-income countries and major population centers. Low-field strength scanners have drawn renewed academic, industry, and philanthropic interest due to advantages that could dramatically increase imaging access, including lower cost and portability. Nevertheless, low-field MRI still faces inherent limitations in image quality that come with decreased signal. In this article, we review advantages and disadvantages of low-field MRI scanners, describe hardware and software innovations that accentuate advantages and mitigate disadvantages, and consider clinical applications for a new generation of low-field devices. In our review, we explore how these devices are being or could be used for high acuity brain imaging, outpatient neuroimaging, MRI-guided procedures, pediatric imaging, and musculoskeletal imaging. Challenges for their successful clinical translation include selecting and validating appropriate use cases, integrating with standards of care in high resource settings, expanding options with actionable information in low resource settings, and facilitating health care providers and clinical practice in new ways. By embracing both the promise and challenges of low-field MRI, clinicians and researchers have an opportunity to transform medical care for patients around the world. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.
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Affiliation(s)
- Thomas Campbell Arnold
- Department of Bioengineering, School of Engineering & Applied ScienceUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Neuroengineering and TherapeuticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Colbey W. Freeman
- Department of Radiology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Brian Litt
- Center for Neuroengineering and TherapeuticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Neurology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Joel M. Stein
- Center for Neuroengineering and TherapeuticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Radiology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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21
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Muacevic A, Adler JR, Marino MA, Maniakhina L, Li JJ, Ku A, Ko K, Miulli DE. Utilization of Portable Brain Magnetic Resonance Imaging in an Acute Care Setting. Cureus 2022; 14:e33067. [PMID: 36726935 PMCID: PMC9886369 DOI: 10.7759/cureus.33067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 12/27/2022] [Indexed: 12/29/2022] Open
Abstract
Background Magnetic resonance imaging (MRI) is an important noninvasive diagnostic tool used in multiple facets of medicine, especially in the assessment of the neurological system with increasing usage over the past decades. Advancement in technology has led to the creation of portable MRI (pMRI) that was cleared for use recently. Methodology A prospectively collected retrospective study was conducted at a single institution to include patients aged >18 years, admitted to the hospital, and requiring MRI for any brain pathology. pMRI was completed using portable MRI. Traditional MRI was completed with a standard 1.5T MRI, and when possible, the results of the two studies were compared. Results We obtained pMRI on 20 patients, with a total of 22 scans completed. Notably, on the pMRI, we were able to identify midline structures to determine midline shifts, identify the size of ventricles, and see large pathologies, including ischemic and hemorrhagic strokes, edema, and tumors. Patients with implants or electrodes in and around the calvarium sometimes pose challenges to image acquisition. Conclusions Portable brain MRI is a practical and useful technology that can provide immediate information about the head, especially in an acute care setting. Portable brain MRI has a lower resolution and quality of imaging compared to that of transitional MRI, and therefore, it is not a replacement for traditional MRI.
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