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Bauer T, Olbrich S, Groteklaes A, Lehnen NC, Zidan M, Lange A, Bisten J, Walger L, Faber J, Bruchhausen W, Vollmuth P, Herrlinger U, Radbruch A, Surges R, Sabir H, Rüber T. Proof of concept: Portable ultra-low-field magnetic resonance imaging for the diagnosis of epileptogenic brain pathologies. Epilepsia 2024. [PMID: 39470733 DOI: 10.1111/epi.18171] [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: 08/19/2024] [Revised: 10/12/2024] [Accepted: 10/14/2024] [Indexed: 11/01/2024]
Abstract
OBJECTIVE High-field magnetic resonance imaging (MRI) is a standard in the diagnosis of epilepsy. However, high costs and technical barriers have limited adoption in low- and middle-income countries. Even in high-income nations, many individuals with epilepsy face delays in undergoing MRI. Recent advancements in ultra-low-field (ULF) MRI technology, particularly the development of portable scanners, offer a promising solution to the limited accessibility of MRI. In this study, we present and evaluate the imaging capability of ULF MRI in detecting structural abnormalities typically associated with epilepsy and compare it to high-field MRI at 3 T. METHODS Data collection was conducted within 3 consecutive weeks at the University Hospital Bonn. Inclusion criteria were a minimum age of 18 years, diagnosed epilepsy, and clinical high-field MRI with abnormalities. We used a .064 T Swoop portable MR Imaging System. Both high-field MRI and ULF MRI scans were evaluated independently by two experienced neuroradiologists as part of their clinical routine, comparing pathology detection and diagnosis completeness. RESULTS Twenty-three individuals with epilepsy were recruited. One subject presented with a dual pathology. Across the entire cohort, in 17 of 24 (71%) pathologies, an anomaly colocalizing with the actual lesion was observed on ULF MRI. For 11 of 24 (46%) pathologies, the full diagnosis could be made based on ULF MRI. Tumors and posttraumatic lesions could be diagnosed best on ULF MRI, whereas cortical dysplasia and other focal pathologies were the least well diagnosed. SIGNIFICANCE This single-center series of individuals with epilepsy demonstrates the feasibility and utility of ULF MRI for the field of epileptology. Its integration into epilepsy care offers transformative potential, particularly in resource-limited settings. Further research is needed to position ULF MRI within imaging modalities in the diagnosis of epilepsy.
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Affiliation(s)
- Tobias Bauer
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Simon Olbrich
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - Anne Groteklaes
- Department of Neonatology and Pediatric Intensive Care, University Hospital Bonn, Bonn, Germany
| | | | - Mousa Zidan
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
| | - Annalena Lange
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- Institute for Computer Science, University of Bonn, Bonn, Germany
- Section for Global Health, Institute for Hygiene and Public Health, University Hospital Bonn, Bonn, Germany
| | - Justus Bisten
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- Institute for Computer Science, University of Bonn, Bonn, Germany
| | - Lennart Walger
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - Jennifer Faber
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, University Hospital Bonn, Bonn, Germany
| | - Walter Bruchhausen
- Section for Global Health, Institute for Hygiene and Public Health, University Hospital Bonn, Bonn, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
| | | | - Alexander Radbruch
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Center for Medical Data Usability and Translation, University of Bonn, Bonn, Germany
| | - Rainer Surges
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - Hemmen Sabir
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neonatology and Pediatric Intensive Care, University Hospital Bonn, Bonn, Germany
| | - Theodor Rüber
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Center for Medical Data Usability and Translation, University of Bonn, Bonn, Germany
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Chowdhury MRH, Ahmed F, Oladun C, Adelabu I, Abdurraheem A, Nantogma S, Birchall JR, Gafar TA, Chekmenev YA, Nikolaou P, Barlow MJ, Goodson BM, Shcherbakov A, Chekmenev EY. Low-Cost Purpose-Built Ultra-Low-Field NMR Spectrometer. Anal Chem 2024; 96:16724-16734. [PMID: 39378166 PMCID: PMC11506762 DOI: 10.1021/acs.analchem.4c03149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2024]
Abstract
Low-field NMR has emerged as a new analytical technique for the investigation of molecular structure and dynamics. Here, we introduce a highly integrated ultralow-frequency NMR spectrometer designed for the purpose of ultralow-field NMR polarimetry of hyperpolarized contrast media. The device measures 10 cm × 10 cm × 2.0 cm and weighs only 370 g. The spectrometer's aluminum enclosure contains all components, including an RF amplifier. The device has four ports for connecting to a high-impedance RF transmit-receive coil, a trigger input, a USB port for connectivity to a PC computer, and an auxiliary RS-485/24VDC port for system integration with other devices. The NMR spectrometer is configured for a pulse-wait-acquire-recover pulse sequence, and key sequence parameters are readily controlled by a graphical user interface (GUI) of a Windows-based PC computer. The GUI also displays the time-domain and Fourier-transformed NMR signal and allows autosaving of NMR data as a CSV file. Alternatively, the RS485 communication line allows for operating the device with sequence parameter control and data processing directly on the spectrometer board in a fully automated and integrated manner. The NMR spectrometer, equipped with a 250 ksamples/s 17-bit analog-to-digital signal converter, can perform acquisition in the 1-125 kHz frequency range. The utility of the device is demonstrated for NMR polarimetry of hyperpolarized 129Xe gas and [1-13C]pyruvate contrast media (which was compared to the 13C polarimetry using a more established technology of benchtop 13C NMR spectroscopy, and yielded similar results), allowing reproducible quantification of polarization values and relaxation dynamics. The cost of the device components is only ∼$200, offering a low-cost integrated NMR spectrometer that can be deployed as a plug-and-play device for a wide range of applications in hyperpolarized contrast media production─and beyond.
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Affiliation(s)
- Md Raduanul H. Chowdhury
- Department of Chemistry, Integrative Biosciences (Ibio), Wayne State University, Karmanos Cancer Institute (KCI), Detroit, Michigan 48202, United States
| | - Firoz Ahmed
- Department of Chemistry, Integrative Biosciences (Ibio), Wayne State University, Karmanos Cancer Institute (KCI), Detroit, Michigan 48202, United States
| | - Clementinah Oladun
- Department of Chemistry, Integrative Biosciences (Ibio), Wayne State University, Karmanos Cancer Institute (KCI), Detroit, Michigan 48202, United States
| | - Isaiah Adelabu
- Department of Chemistry, Integrative Biosciences (Ibio), Wayne State University, Karmanos Cancer Institute (KCI), Detroit, Michigan 48202, United States
| | - Abubakar Abdurraheem
- Department of Chemistry, Integrative Biosciences (Ibio), Wayne State University, Karmanos Cancer Institute (KCI), Detroit, Michigan 48202, United States
| | - Shiraz Nantogma
- Department of Chemistry, Integrative Biosciences (Ibio), Wayne State University, Karmanos Cancer Institute (KCI), Detroit, Michigan 48202, United States
| | - Jonathan R. Birchall
- Department of Chemistry, Integrative Biosciences (Ibio), Wayne State University, Karmanos Cancer Institute (KCI), Detroit, Michigan 48202, United States
| | - Tobi Abdulbasit Gafar
- School of Chemical & Biomolecular Sciences and Materials Technology Center, Southern Illinois University, Carbondale, Illinois 62901, United States
| | | | | | - Michael J. Barlow
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, NG7 2RD, United Kingdom
| | - Boyd M. Goodson
- School of Chemical & Biomolecular Sciences and Materials Technology Center, Southern Illinois University, Carbondale, Illinois 62901, United States
| | - Anton Shcherbakov
- XeUS Technologies LTD, Nicosia 2312, Cyprus
- Custom Medical Systems (CMS) LTD, Nicosia 2312, Cyprus
| | - Eduard Y. Chekmenev
- Department of Chemistry, Integrative Biosciences (Ibio), Wayne State University, Karmanos Cancer Institute (KCI), Detroit, Michigan 48202, United States
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Preller KH, Scholpp J, Wunder A, Rosenbrock H. Neuroimaging Biomarkers for Drug Discovery and Development in Schizophrenia. Biol Psychiatry 2024; 96:666-673. [PMID: 38272287 DOI: 10.1016/j.biopsych.2024.01.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 12/19/2023] [Accepted: 01/14/2024] [Indexed: 01/27/2024]
Abstract
Schizophrenia is a chronic mental illness that affects up to 1% of the population. While efficacious therapies are available for positive symptoms, effective treatment of cognitive and negative symptoms remains an unmet need after decades of research. New developments in the field of neuroimaging are accelerating our knowledge gain regarding the underlying pathophysiology of symptoms in schizophrenia and psychosis spectrum disorders, inspiring new targets for drug development. However, no validated and qualified biomarkers are currently available to support the development of new therapeutics. This review summarizes the current use of neuroimaging technology in clinical drug development for psychotic disorders. As exemplified by drug development programs that target NMDA receptor hypofunction, neuroimaging results play a critical role in target discovery and establishing target engagement and dose selection. Furthermore, pharmacological neuroimaging may provide response biomarkers that allow for early decision making in proof-of-concept studies that leverage pharmacological challenge models in healthy volunteers. That said, while response and predictive biomarkers are starting to be evaluated in patient populations, they continue to play a limited role. Novel approaches to neuroimaging data acquisition and analysis may aid the establishment of biomarkers that are predictive at the individual level in the future. Nevertheless, various gaps in knowledge need to be addressed and biomarkers need to be validated to establish them as "fit for purpose" in drug development.
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Affiliation(s)
- Katrin H Preller
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany; Boehringer Ingelheim (Schweiz) GmbH, Basel, Switzerland.
| | - Joachim Scholpp
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Andreas Wunder
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Holger Rosenbrock
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
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Sorby-Adams A, Guo J, de Havenon A, Payabvash S, Sze G, Pinter NK, Jaikumar V, Siddiqui A, Baldassano S, Garcia-Guarniz AL, Zabinska J, Lalwani D, Peasley E, Goldstein JN, Nelson OK, Schaefer PW, Wira CR, Pitts J, Lee V, Muir KW, Nimjee SM, Kirsch J, Eugenio Iglesias J, Rosen MS, Sheth KN, Kimberly WT. Diffusion-Weighted Imaging Fluid-Attenuated Inversion Recovery Mismatch on Portable, Low-Field Magnetic Resonance Imaging Among Acute Stroke Patients. Ann Neurol 2024; 96:321-331. [PMID: 38738750 PMCID: PMC11293843 DOI: 10.1002/ana.26954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 04/26/2024] [Accepted: 04/28/2024] [Indexed: 05/14/2024]
Abstract
OBJECTIVE For stroke patients with unknown time of onset, mismatch between diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) can guide thrombolytic intervention. However, access to MRI for hyperacute stroke is limited. Here, we sought to evaluate whether a portable, low-field (LF)-MRI scanner can identify DWI-FLAIR mismatch in acute ischemic stroke. METHODS Eligible patients with a diagnosis of acute ischemic stroke underwent LF-MRI acquisition on a 0.064-T scanner within 24 h of last known well. Qualitative and quantitative metrics were evaluated. Two trained assessors determined the visibility of stroke lesions on LF-FLAIR. An image coregistration pipeline was developed, and the LF-FLAIR signal intensity ratio (SIR) was derived. RESULTS The study included 71 patients aged 71 ± 14 years and a National Institutes of Health Stroke Scale of 6 (interquartile range 3-14). The interobserver agreement for identifying visible FLAIR hyperintensities was high (κ = 0.85, 95% CI 0.70-0.99). Visual DWI-FLAIR mismatch had a 60% sensitivity and 82% specificity for stroke patients <4.5 h, with a negative predictive value of 93%. LF-FLAIR SIR had a mean value of 1.18 ± 0.18 <4.5 h, 1.24 ± 0.39 4.5-6 h, and 1.40 ± 0.23 >6 h of stroke onset. The optimal cut-point for LF-FLAIR SIR was 1.15, with 85% sensitivity and 70% specificity. A cut-point of 6.6 h was established for a FLAIR SIR <1.15, with an 89% sensitivity and 62% specificity. INTERPRETATION A 0.064-T portable LF-MRI can identify DWI-FLAIR mismatch among patients with acute ischemic stroke. Future research is needed to prospectively validate thresholds and evaluate a role of LF-MRI in guiding thrombolysis among stroke patients with uncertain time of onset. ANN NEUROL 2024;96:321-331.
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Affiliation(s)
- Annabel Sorby-Adams
- Department of Neurology and the Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Jennifer Guo
- Department of Neurology and the Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Adam de Havenon
- Department of Neurology, Yale Center for Brain & Mind Health, Yale School of Medicine, New Haven, CT, USA
| | - Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Gordon Sze
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Nandor K. Pinter
- Department of Radiology, Jacobs School of Medicine & Biomedical Sciences, University of Buffalo, Buffalo, New York, USA
- Department of Neurosurgery, Jacobs School of Medicine & Biomedical Sciences, University of Buffalo, Buffalo, New York, USA
| | - Vinay Jaikumar
- Department of Neurosurgery, Jacobs School of Medicine & Biomedical Sciences, University of Buffalo, Buffalo, New York, USA
| | - Adnan Siddiqui
- Department of Neurosurgery, Jacobs School of Medicine & Biomedical Sciences, University of Buffalo, Buffalo, New York, USA
| | - Steven Baldassano
- Division of Neuroradiology, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Ana-Lucia Garcia-Guarniz
- Department of Neurology and the Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Julia Zabinska
- Department of Neurology, Yale Center for Brain & Mind Health, Yale School of Medicine, New Haven, CT, USA
| | - Dheeraj Lalwani
- Department of Neurology, Yale Center for Brain & Mind Health, Yale School of Medicine, New Haven, CT, USA
| | - Emma Peasley
- Department of Neurology, Yale Center for Brain & Mind Health, Yale School of Medicine, New Haven, CT, USA
| | - Joshua N. Goldstein
- Department of Emergency Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Olivia K. Nelson
- Department of Emergency Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Pamela W. Schaefer
- Division of Neuroradiology, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Charles R. Wira
- Department of Emergency Medicine, Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut, USA
| | - John Pitts
- Hyperfine Incorporated, Guilford, Connecticut, USA
| | - Vivien Lee
- Wexner Medical Center, Ohio State University, Columbus, Ohio, USA
| | - Keith W. Muir
- School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
| | - Shahid M. Nimjee
- Wexner Medical Center, Ohio State University, Columbus, Ohio, USA
| | - John Kirsch
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Juan Eugenio Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Centre for Medical Image Computing, University College London, London, UK
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Matthew S. Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Kevin N. Sheth
- Department of Neurology, Yale Center for Brain & Mind Health, Yale School of Medicine, New Haven, CT, USA
| | - W. Taylor Kimberly
- Department of Neurology and the Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
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5
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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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Shen S, Koonjoo N, Longarino FK, Lamb LR, Villa Camacho JC, Hornung TPP, Ogier SE, Yan S, Bortfeld TR, Saksena MA, Keenan KE, Rosen MS. Breast imaging with an ultra-low field MRI scanner: a pilot study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.01.24305081. [PMID: 38633799 PMCID: PMC11023648 DOI: 10.1101/2024.04.01.24305081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Breast cancer screening is necessary to reduce mortality due to undetected breast cancer. Current methods have limitations, and as a result many women forego regular screening. Magnetic resonance imaging (MRI) can overcome most of these limitations, but access to conventional MRI is not widely available for routine annual screening. Here, we used an MRI scanner operating at ultra-low field (ULF) to image the left breasts of 11 women (mean age, 35 years ±13 years) in the prone position. Three breast radiologists reviewed the imaging and were able to discern the breast outline and distinguish fibroglandular tissue (FGT) from intramammary adipose tissue. Additionally, the expert readers agreed on their assessment of the breast tissue pattern including fatty, scattered FGT, heterogeneous FGT, and extreme FGT. This preliminary work demonstrates that ULF breast MRI is feasible and may be a potential option for comfortable, widely deployable, and low-cost breast cancer diagnosis and screening.
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Cho SM, Khanduja S, Kim J, Kang JK, Briscoe J, Arlinghaus LR, Dinh K, Kim BS, Sair HI, Wandji ACN, Moreno E, Torres G, Gavito-Higuera J, Choi HA, Pitts J, Gusdon AM, Whitman GJ. Detection of Acute Brain Injury in Intensive Care Unit Patients on ECMO Support Using Ultra-Low-Field Portable MRI: A Retrospective Analysis Compared to Head CT. Diagnostics (Basel) 2024; 14:606. [PMID: 38535027 PMCID: PMC10968816 DOI: 10.3390/diagnostics14060606] [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: 02/16/2024] [Revised: 03/04/2024] [Accepted: 03/09/2024] [Indexed: 09/05/2024] Open
Abstract
Early detection of acute brain injury (ABI) is critical to intensive care unit (ICU) patient management and intervention to decrease major complications. Head CT (HCT) is the standard of care for the assessment of ABI in ICU patients; however, it has limited sensitivity compared to MRI. We retrospectively compared the ability of ultra-low-field portable MR (ULF-pMR) and head HCT, acquired within 24 h of each other, to detect ABI in ICU patients supported on extracorporeal membrane oxygenation (ECMO). A total of 17 adult patients (median age 55 years; 47% male) were included in the analysis. Of the 17 patients assessed, ABI was not observed on either ULF-pMR or HCT in eight patients (47%). ABI was observed in the remaining nine patients with a total of 10 events (8 ischemic, 2 hemorrhagic). Of the eight ischemic events, ULF-pMR observed all eight, while HCT only observed four events. Regarding hemorrhagic stroke, ULF-pMR observed only one of them, while HCT observed both. ULF-pMR outperformed HCT for the detection of ABI, especially ischemic injury, and may offer diagnostic advantages for ICU patients. The lack of sensitivity to hemorrhage may improve with modification of the imaging acquisition program.
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Affiliation(s)
- Sung-Min Cho
- Division of Cardiac Surgery, Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Division of Neuroscience Critical Care, Departments of Neurosurgery, Anesthesiology, Critical Care Medicine, The Johns Hopkins Hospital, Baltimore, MD 21287, USA
| | - Shivalika Khanduja
- Division of Cardiac Surgery, Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Jiah Kim
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Jin Kook Kang
- Division of Cardiac Surgery, Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Jessica Briscoe
- Division of Cardiac Surgery, Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | | | - Kha Dinh
- Divisions of Pulmonary, Critical Care and Sleep Medicine, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Department of Advanced Cardiopulmonary Therapies and Transplantation, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Bo Soo Kim
- Division of Cardiac Surgery, Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- Division of Pulmonary and Critical Care Medicine, Johns Hopkins Medicine, Baltimore, MD 21205, USA
| | - Haris I Sair
- Division of Neuroradiology, The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Audrey-Carelle N Wandji
- Division of Neurocritical Care, Department of Neurosurgery, McGovern School of Medicine, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Elena Moreno
- Division of Neurocritical Care, Department of Neurosurgery, McGovern School of Medicine, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Glenda Torres
- Division of Neurocritical Care, Department of Neurosurgery, McGovern School of Medicine, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Jose Gavito-Higuera
- Department of Diagnostic and Interventional Imaging, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Huimahn A Choi
- Division of Neurocritical Care, Department of Neurosurgery, McGovern School of Medicine, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - John Pitts
- Hyperfine, Inc., Guilford, CT 06437, USA
| | - Aaron M Gusdon
- Division of Neurocritical Care, Department of Neurosurgery, McGovern School of Medicine, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Glenn J Whitman
- Division of Cardiac Surgery, Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
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MacCulloch K, Browning A, Bedoya DOG, McBride SJ, Abdulmojeed MB, Dedesma C, Goodson BM, Rosen MS, Chekmenev EY, Yen YF, TomHon P, Theis T. Facile hyperpolarization chemistry for molecular imaging and metabolic tracking of [1- 13C]pyruvate in vivo. JOURNAL OF MAGNETIC RESONANCE OPEN 2023; 16-17:100129. [PMID: 38090022 PMCID: PMC10715622 DOI: 10.1016/j.jmro.2023.100129] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Hyperpolarization chemistry based on reversible exchange of parahydrogen, also known as Signal Amplification By Reversible Exchange (SABRE), is a particularly simple approach to attain high levels of nuclear spin hyperpolarization, which can enhance NMR and MRI signals by many orders of magnitude. SABRE has received significant attention in the scientific community since its inception because of its relative experimental simplicity and its broad applicability to a wide range of molecules, however in vivo detection of molecular probes hyperpolarized by SABRE has remained elusive. Here we describe a first demonstration of SABRE-hyperpolarized contrast detected in vivo, specifically using hyperpolarized [1-13C]pyruvate. Biocompatible formulations of hyperpolarized [1-13C]pyruvate in, both, methanol-water mixtures, and ethanol-water mixtures followed by dilution with saline and catalyst filtration were prepared and injected into healthy Sprague Dawley and Wistar rats. Effective hyperpolarization-catalyst removal was performed with silica filters without major losses in hyperpolarization. Metabolic conversion of pyruvate to lactate, alanine, and bicarbonate was detected in vivo. Pyruvate-hydrate was also observed as minor byproduct. Measurements were performed on the liver and kidney at 4.7 T via time-resolved spectroscopy and chemical-shift-resolved MRI. In addition, whole-body metabolic measurements were obtained using a cryogen-free 1.5 T MRI system, illustrating the utility of combining lower-cost MRI systems with simple, low-cost hyperpolarization chemistry to develop safe, and scalable molecular imaging.
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Affiliation(s)
- Keilian MacCulloch
- Department of Chemistry, North Carolina State University, Raleigh, NC, 27695,USA
| | - Austin Browning
- Department of Chemistry, North Carolina State University, Raleigh, NC, 27695,USA
| | - David O. Guarin Bedoya
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Stephen J. McBride
- Department of Chemistry, North Carolina State University, Raleigh, NC, 27695,USA
| | | | - Carlos Dedesma
- Vizma Life Sciences Inc., Chapel Hill, NC, 27514, United States
| | - Boyd M. Goodson
- School of Chemical & Biomolecular Sciences and Materials Technology Center, Southern Illinois University, Carbondale, IL, 62901, USA
| | - Matthew S. Rosen
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Eduard Y. Chekmenev
- Department of Chemistry, Integrative Bio-sciences (Ibio), Karmanos Cancer Institute (KCI), Wayne State University, Detroit, MI 48202, USA
- Russian Academy of Sciences, 119991 Moscow, Russia
| | - Yi-Fen Yen
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Patrick TomHon
- Vizma Life Sciences Inc., Chapel Hill, NC, 27514, United States
| | - Thomas Theis
- Department of Chemistry, North Carolina State University, Raleigh, NC, 27695,USA
- Department of Physics, North Carolina State University, Raleigh, NC 27606, USA
- Joint UNC & NC State Department of Biomedical Engineering, North Carolina State University, Raleigh, NC 27606, USA
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9
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Mazurek MH, Parasuram NR, Peng TJ, Beekman R, Yadlapalli V, Sorby-Adams AJ, Lalwani D, Zabinska J, Gilmore EJ, Petersen NH, Falcone GJ, Sujijantarat N, Matouk C, Payabvash S, Sze G, Schiff SJ, Iglesias JE, Rosen MS, de Havenon A, Kimberly WT, Sheth KN. Detection of Intracerebral Hemorrhage Using Low-Field, Portable Magnetic Resonance Imaging in Patients With Stroke. Stroke 2023; 54:2832-2841. [PMID: 37795593 PMCID: PMC11103256 DOI: 10.1161/strokeaha.123.043146] [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: 03/20/2023] [Revised: 09/07/2023] [Accepted: 09/13/2023] [Indexed: 10/06/2023]
Abstract
BACKGROUND Neuroimaging is essential for detecting spontaneous, nontraumatic intracerebral hemorrhage (ICH). Recent data suggest ICH can be characterized using low-field magnetic resonance imaging (MRI). Our primary objective was to investigate the sensitivity and specificity of ICH on a 0.064T portable MRI (pMRI) scanner using a methodology that provided clinical information to inform rater interpretations. As a secondary aim, we investigated whether the incorporation of a deep learning (DL) reconstruction algorithm affected ICH detection. METHODS The pMRI device was deployed at Yale New Haven Hospital to examine patients presenting with stroke symptoms from October 26, 2020 to February 21, 2022. Three raters independently evaluated pMRI examinations. Raters were provided the images alongside the patient's clinical information to simulate real-world context of use. Ground truth was the closest conventional computed tomography or 1.5/3T MRI. Sensitivity and specificity results were grouped by DL and non-DL software to investigate the effects of software advances. RESULTS A total of 189 exams (38 ICH, 89 acute ischemic stroke, 8 subarachnoid hemorrhage, 3 primary intraventricular hemorrhage, 51 no intracranial abnormality) were evaluated. Exams were correctly classified as positive or negative for ICH in 185 of 189 cases (97.9% overall accuracy). ICH was correctly detected in 35 of 38 cases (92.1% sensitivity). Ischemic stroke and no intracranial abnormality cases were correctly identified as blood-negative in 139 of 140 cases (99.3% specificity). Non-DL scans had a sensitivity and specificity for ICH of 77.8% and 97.1%, respectively. DL scans had a sensitivity and specificity for ICH of 96.6% and 99.3%, respectively. CONCLUSIONS These results demonstrate improvements in ICH detection accuracy on pMRI that may be attributed to the integration of clinical information in rater review and the incorporation of a DL-based algorithm. The use of pMRI holds promise in providing diagnostic neuroimaging for patients with ICH.
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Affiliation(s)
- Mercy H. Mazurek
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | | | - Teng J. Peng
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Rachel Beekman
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | | | - Annabel J. Sorby-Adams
- Department of Neurology, Division of Neurocritical Care, Massachusetts General Hospital, Boston, MA, USA
| | - Dheeraj Lalwani
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Julia Zabinska
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Emily J. Gilmore
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Nils H. Petersen
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Guido J. Falcone
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | | | - Charles Matouk
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Sam Payabvash
- Department of Radiology, Yale University School of Medicine, New Haven, CT, USA
| | - Gordon Sze
- Department of Radiology, Yale University School of Medicine, New Haven, CT, USA
| | - Steven J. Schiff
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
- Yale Center for Brain & Mind Heath, Yale School of Medicine, New Haven, CT, USA
| | - Juan Eugenio Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Centre for Medical Image Computing, University College London, London, UK
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Matthew S. Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Adam de Havenon
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - W. Taylor Kimberly
- Department of Neurology, Division of Neurocritical Care, Massachusetts General Hospital, Boston, MA, USA
| | - Kevin N. Sheth
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
- Yale Center for Brain & Mind Heath, Yale School of Medicine, New Haven, CT, USA
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10
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Campbell-Washburn AE, Keenan KE, Hu P, Mugler JP, Nayak KS, Webb AG, Obungoloch J, Sheth KN, Hennig J, Rosen MS, Salameh N, Sodickson DK, Stein JM, Marques JP, Simonetti OP. Low-field MRI: A report on the 2022 ISMRM workshop. Magn Reson Med 2023; 90:1682-1694. [PMID: 37345725 PMCID: PMC10683532 DOI: 10.1002/mrm.29743] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 04/21/2023] [Accepted: 05/17/2023] [Indexed: 06/23/2023]
Abstract
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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Affiliation(s)
- Adrienne E Campbell-Washburn
- Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Kathryn E Keenan
- Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, Colorado, USA
| | - Peng Hu
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - John P Mugler
- Department of Radiology & Medical Imaging, Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Krishna S Nayak
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | - Andrew G Webb
- Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
| | | | - Kevin N Sheth
- Division of Neurocritical Care and Emergency Neurology, Departments of Neurology and Neurosurgery, and the Yale Center for Brain and Mind Health, Yale School of Medicine, New Haven, Connecticut, USA
| | - Jürgen Hennig
- Dept.of Radiology, Medical Physics, University Medical Center Freiburg, Freiburg, Germany
- Center for Basics in NeuroModulation (NeuroModulBasics), Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Matthew S Rosen
- Massachusetts General Hospital, A. A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, USA
- Department of Physics, Harvard University, Cambridge, Massachusetts, USA
| | - Najat Salameh
- Center for Adaptable MRI Technology (AMT Center), Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland
| | - Daniel K Sodickson
- Department of Radiology, NYU Langone Health, New York, New York, USA
- Center for Advanced Imaging Innovation and Research, NYU Langone Health, New York, New York, USA
| | - Joel M Stein
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Orlando P Simonetti
- Division of Cardiovascular Medicine, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, Ohio, USA
- Department of Radiology, The Ohio State University, Columbus, Ohio, USA
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11
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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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12
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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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13
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Altaf A, Baqai MWS, Urooj F, Alam MS, Aziz HF, Mubarak F, Knopp EA, Siddiqui KM, Enam SA. Utilization of an ultra-low-field, portable magnetic resonance imaging for brain tumor assessment in lower middle-income countries. Surg Neurol Int 2023; 14:260. [PMID: 37560587 PMCID: PMC10408621 DOI: 10.25259/sni_123_2023] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 05/04/2023] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND Access to neuroimaging is limited in low-middle-income countries (LMICs) due to financial and resource constraints. A new, ultra-low-field, low-cost, and portable magnetic resonance imaging (pMRI) device could potentially increase access to imaging in LMICs. CASE DESCRIPTION We have presented the first brain tumor case scanned using an Ultra-low-field pMRI at Aga Khan University Hospital in Karachi, Pakistan. CONCLUSION The imaging results suggest that the pMRI device can aid in neuroradiological diagnosis in resource-constrained settings. Further, research is needed to assess its compatibility for imaging other neurological disorders and compare its results with conventional MRI results.
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Affiliation(s)
- Ahmed Altaf
- Department of Neurosurgery, Aga Khan University Hospital, Karachi, Sindh, Pakistan
| | | | - Faiza Urooj
- Department of Neurosurgery, Aga Khan University Hospital, Karachi, Sindh, Pakistan
| | - Muhammad Sami Alam
- Department of Radiology, National Medical Centre, Karachi, Sindh, Pakistan
| | - Hafiza Fatima Aziz
- Department of Neurosurgery, Aga Khan University Hospital, Karachi, Sindh, Pakistan
| | - Fatima Mubarak
- Department of Radiology, The Aga Khan University Hospital, Karachi, Sindh, Pakistan
| | - Edmond A. Knopp
- Department of Radiology, Hyperfine, Guilford, Connecticut, United States
| | - Khan M. Siddiqui
- Department of Radiology, Hyperfine, Guilford, Connecticut, United States
| | - Syed Ather Enam
- Department of Neurosurgery, Aga Khan University Hospital, Karachi, Sindh, Pakistan
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14
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Oberdick SD, Jordanova KV, Lundstrom JT, Parigi G, Poorman ME, Zabow G, Keenan KE. Iron oxide nanoparticles as positive T 1 contrast agents for low-field magnetic resonance imaging at 64 mT. Sci Rep 2023; 13:11520. [PMID: 37460669 DOI: 10.1038/s41598-023-38222-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 07/05/2023] [Indexed: 07/20/2023] Open
Abstract
We have investigated the efficacy of superparamagnetic iron oxide nanoparticles (SPIONs) as positive T1 contrast agents for low-field magnetic resonance imaging (MRI) at 64 millitesla (mT). Iron oxide-based agents, such as the FDA-approved ferumoxytol, were measured using a variety of techniques to evaluate T1 contrast at 64 mT. Additionally, we characterized monodispersed carboxylic acid-coated SPIONs with a range of diameters (4.9-15.7 nm) in order to understand size-dependent properties of T1 contrast at low-field. MRI contrast properties were measured using 64 mT MRI, magnetometry, and nuclear magnetic resonance dispersion (NMRD). We also measured MRI contrast at 3 T to provide comparison to a standard clinical field strength. SPIONs have the capacity to perform well as T1 contrast agents at 64 mT, with measured longitudinal relaxivity (r1) values of up to 67 L mmol-1 s-1, more than an order of magnitude higher than corresponding r1 values at 3 T. The particles exhibit size-dependent longitudinal relaxivities and outperform a commercial Gd-based agent (gadobenate dimeglumine) by more than eight-fold at physiological temperatures. Additionally, we characterize the ratio of transverse to longitudinal relaxivity, r2/r1 and find that it is ~ 1 for the SPION based agents at 64 mT, indicating a favorable balance of relaxivities for T1-weighted contrast imaging. We also correlate the magnetic and structural properties of the particles with models of nanoparticle relaxivity to understand generation of T1 contrast. These experiments show that SPIONs, at low fields being targeted for point-of-care low-field MRI systems, have a unique combination of magnetic and structural properties that produce large T1 relaxivities.
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Affiliation(s)
- Samuel D Oberdick
- Department of Physics, University of Colorado, Boulder, CO, 80309, USA.
- National Institute of Standards and Technology, Boulder, CO, 80305, USA.
| | | | - John T Lundstrom
- Department of Physics, University of Colorado, Boulder, CO, 80309, USA
- National Institute of Standards and Technology, Boulder, CO, 80305, USA
| | - Giacomo Parigi
- Magnetic Resonance Center (CERM), University of Florence, Via Luigi Sacconi 6, 50019, Sesto Fiorentino, Italy
- Department of Chemistry "Ugo Schiff", University of Florence, Via Della Lastruccia 3, 50019, Sesto Fiorentino, Italy
- Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), Via Luigi Sacconi 6, 50019, Sesto Fiorentino, Italy
| | | | - Gary Zabow
- National Institute of Standards and Technology, Boulder, CO, 80305, USA
| | - Kathryn E Keenan
- National Institute of Standards and Technology, Boulder, CO, 80305, USA
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15
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Parasuram NR, Crawford AL, Mazurek MH, Chavva IR, Beekman R, Gilmore EJ, Petersen NH, Payabvash S, Sze G, Eugenio Iglesias J, Omay SB, Matouk C, Longbrake EE, de Havenon A, Schiff SJ, Rosen MS, Kimberly WT, Sheth KN. Future of Neurology & Technology: Neuroimaging Made Accessible Using Low-Field, Portable MRI. Neurology 2023; 100:1067-1071. [PMID: 36720639 PMCID: PMC10259275 DOI: 10.1212/wnl.0000000000207074] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 01/04/2023] [Indexed: 02/02/2023] Open
Abstract
In the 20th century, the advent of neuroimaging dramatically altered the field of neurologic care. However, despite iterative advances since the invention of CT and MRI, little progress has been made to bring MR neuroimaging to the point of care. Recently, the emergence of a low-field (<1 T) portable MRI (pMRI) is setting the stage to revolutionize the landscape of accessible neuroimaging. Users can transport the pMRI into a variety of locations, using a standard 110-220 V wall outlet. In this article, we discuss current applications for pMRI, including in the acute and critical care settings, the barriers to broad implementation, and future opportunities.
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Affiliation(s)
- Nethra R Parasuram
- From the Department of Neurology (N.R.P., A.L.C., M.H.M., I.R.C., R.B., E.J.G., N.H.P., E.E.L., A.d.H., K.N.S.), and Department of Radiology (S.P., G.S.), Yale University School of Medicine, New Haven, CT; Computer Science and Artificial Intelligence Laboratory (CSAIL) (J.E.I.), Massachusetts Institute of Technology, Cambridge; Athinoula A. Martinos Center for Biomedical Imaging (J.E.I., M.S.R.), Massachusetts General Hospital, Charlestown; Department of Neurosurgery (S.B.O., C.M.), Yale University School of Medicine, New Haven, CT; Department of Neurosurgery (S.J.S.), Engineering Science and Mechanics, and Physics, The Pennsylvania State University, University Park; and Division of Neurocritical Care (W.T.K.), Department of Neurology, Massachusetts General Hospital, Boston
| | - Anna L Crawford
- From the Department of Neurology (N.R.P., A.L.C., M.H.M., I.R.C., R.B., E.J.G., N.H.P., E.E.L., A.d.H., K.N.S.), and Department of Radiology (S.P., G.S.), Yale University School of Medicine, New Haven, CT; Computer Science and Artificial Intelligence Laboratory (CSAIL) (J.E.I.), Massachusetts Institute of Technology, Cambridge; Athinoula A. Martinos Center for Biomedical Imaging (J.E.I., M.S.R.), Massachusetts General Hospital, Charlestown; Department of Neurosurgery (S.B.O., C.M.), Yale University School of Medicine, New Haven, CT; Department of Neurosurgery (S.J.S.), Engineering Science and Mechanics, and Physics, The Pennsylvania State University, University Park; and Division of Neurocritical Care (W.T.K.), Department of Neurology, Massachusetts General Hospital, Boston
| | - Mercy H Mazurek
- From the Department of Neurology (N.R.P., A.L.C., M.H.M., I.R.C., R.B., E.J.G., N.H.P., E.E.L., A.d.H., K.N.S.), and Department of Radiology (S.P., G.S.), Yale University School of Medicine, New Haven, CT; Computer Science and Artificial Intelligence Laboratory (CSAIL) (J.E.I.), Massachusetts Institute of Technology, Cambridge; Athinoula A. Martinos Center for Biomedical Imaging (J.E.I., M.S.R.), Massachusetts General Hospital, Charlestown; Department of Neurosurgery (S.B.O., C.M.), Yale University School of Medicine, New Haven, CT; Department of Neurosurgery (S.J.S.), Engineering Science and Mechanics, and Physics, The Pennsylvania State University, University Park; and Division of Neurocritical Care (W.T.K.), Department of Neurology, Massachusetts General Hospital, Boston
| | - Isha R Chavva
- From the Department of Neurology (N.R.P., A.L.C., M.H.M., I.R.C., R.B., E.J.G., N.H.P., E.E.L., A.d.H., K.N.S.), and Department of Radiology (S.P., G.S.), Yale University School of Medicine, New Haven, CT; Computer Science and Artificial Intelligence Laboratory (CSAIL) (J.E.I.), Massachusetts Institute of Technology, Cambridge; Athinoula A. Martinos Center for Biomedical Imaging (J.E.I., M.S.R.), Massachusetts General Hospital, Charlestown; Department of Neurosurgery (S.B.O., C.M.), Yale University School of Medicine, New Haven, CT; Department of Neurosurgery (S.J.S.), Engineering Science and Mechanics, and Physics, The Pennsylvania State University, University Park; and Division of Neurocritical Care (W.T.K.), Department of Neurology, Massachusetts General Hospital, Boston
| | - Rachel Beekman
- From the Department of Neurology (N.R.P., A.L.C., M.H.M., I.R.C., R.B., E.J.G., N.H.P., E.E.L., A.d.H., K.N.S.), and Department of Radiology (S.P., G.S.), Yale University School of Medicine, New Haven, CT; Computer Science and Artificial Intelligence Laboratory (CSAIL) (J.E.I.), Massachusetts Institute of Technology, Cambridge; Athinoula A. Martinos Center for Biomedical Imaging (J.E.I., M.S.R.), Massachusetts General Hospital, Charlestown; Department of Neurosurgery (S.B.O., C.M.), Yale University School of Medicine, New Haven, CT; Department of Neurosurgery (S.J.S.), Engineering Science and Mechanics, and Physics, The Pennsylvania State University, University Park; and Division of Neurocritical Care (W.T.K.), Department of Neurology, Massachusetts General Hospital, Boston
| | - Emily J Gilmore
- From the Department of Neurology (N.R.P., A.L.C., M.H.M., I.R.C., R.B., E.J.G., N.H.P., E.E.L., A.d.H., K.N.S.), and Department of Radiology (S.P., G.S.), Yale University School of Medicine, New Haven, CT; Computer Science and Artificial Intelligence Laboratory (CSAIL) (J.E.I.), Massachusetts Institute of Technology, Cambridge; Athinoula A. Martinos Center for Biomedical Imaging (J.E.I., M.S.R.), Massachusetts General Hospital, Charlestown; Department of Neurosurgery (S.B.O., C.M.), Yale University School of Medicine, New Haven, CT; Department of Neurosurgery (S.J.S.), Engineering Science and Mechanics, and Physics, The Pennsylvania State University, University Park; and Division of Neurocritical Care (W.T.K.), Department of Neurology, Massachusetts General Hospital, Boston
| | - Nils H Petersen
- From the Department of Neurology (N.R.P., A.L.C., M.H.M., I.R.C., R.B., E.J.G., N.H.P., E.E.L., A.d.H., K.N.S.), and Department of Radiology (S.P., G.S.), Yale University School of Medicine, New Haven, CT; Computer Science and Artificial Intelligence Laboratory (CSAIL) (J.E.I.), Massachusetts Institute of Technology, Cambridge; Athinoula A. Martinos Center for Biomedical Imaging (J.E.I., M.S.R.), Massachusetts General Hospital, Charlestown; Department of Neurosurgery (S.B.O., C.M.), Yale University School of Medicine, New Haven, CT; Department of Neurosurgery (S.J.S.), Engineering Science and Mechanics, and Physics, The Pennsylvania State University, University Park; and Division of Neurocritical Care (W.T.K.), Department of Neurology, Massachusetts General Hospital, Boston
| | - Seyedmehdi Payabvash
- From the Department of Neurology (N.R.P., A.L.C., M.H.M., I.R.C., R.B., E.J.G., N.H.P., E.E.L., A.d.H., K.N.S.), and Department of Radiology (S.P., G.S.), Yale University School of Medicine, New Haven, CT; Computer Science and Artificial Intelligence Laboratory (CSAIL) (J.E.I.), Massachusetts Institute of Technology, Cambridge; Athinoula A. Martinos Center for Biomedical Imaging (J.E.I., M.S.R.), Massachusetts General Hospital, Charlestown; Department of Neurosurgery (S.B.O., C.M.), Yale University School of Medicine, New Haven, CT; Department of Neurosurgery (S.J.S.), Engineering Science and Mechanics, and Physics, The Pennsylvania State University, University Park; and Division of Neurocritical Care (W.T.K.), Department of Neurology, Massachusetts General Hospital, Boston
| | - Gordon Sze
- From the Department of Neurology (N.R.P., A.L.C., M.H.M., I.R.C., R.B., E.J.G., N.H.P., E.E.L., A.d.H., K.N.S.), and Department of Radiology (S.P., G.S.), Yale University School of Medicine, New Haven, CT; Computer Science and Artificial Intelligence Laboratory (CSAIL) (J.E.I.), Massachusetts Institute of Technology, Cambridge; Athinoula A. Martinos Center for Biomedical Imaging (J.E.I., M.S.R.), Massachusetts General Hospital, Charlestown; Department of Neurosurgery (S.B.O., C.M.), Yale University School of Medicine, New Haven, CT; Department of Neurosurgery (S.J.S.), Engineering Science and Mechanics, and Physics, The Pennsylvania State University, University Park; and Division of Neurocritical Care (W.T.K.), Department of Neurology, Massachusetts General Hospital, Boston
| | - Juan Eugenio Iglesias
- From the Department of Neurology (N.R.P., A.L.C., M.H.M., I.R.C., R.B., E.J.G., N.H.P., E.E.L., A.d.H., K.N.S.), and Department of Radiology (S.P., G.S.), Yale University School of Medicine, New Haven, CT; Computer Science and Artificial Intelligence Laboratory (CSAIL) (J.E.I.), Massachusetts Institute of Technology, Cambridge; Athinoula A. Martinos Center for Biomedical Imaging (J.E.I., M.S.R.), Massachusetts General Hospital, Charlestown; Department of Neurosurgery (S.B.O., C.M.), Yale University School of Medicine, New Haven, CT; Department of Neurosurgery (S.J.S.), Engineering Science and Mechanics, and Physics, The Pennsylvania State University, University Park; and Division of Neurocritical Care (W.T.K.), Department of Neurology, Massachusetts General Hospital, Boston
| | - Sacit B Omay
- From the Department of Neurology (N.R.P., A.L.C., M.H.M., I.R.C., R.B., E.J.G., N.H.P., E.E.L., A.d.H., K.N.S.), and Department of Radiology (S.P., G.S.), Yale University School of Medicine, New Haven, CT; Computer Science and Artificial Intelligence Laboratory (CSAIL) (J.E.I.), Massachusetts Institute of Technology, Cambridge; Athinoula A. Martinos Center for Biomedical Imaging (J.E.I., M.S.R.), Massachusetts General Hospital, Charlestown; Department of Neurosurgery (S.B.O., C.M.), Yale University School of Medicine, New Haven, CT; Department of Neurosurgery (S.J.S.), Engineering Science and Mechanics, and Physics, The Pennsylvania State University, University Park; and Division of Neurocritical Care (W.T.K.), Department of Neurology, Massachusetts General Hospital, Boston
| | - Charles Matouk
- From the Department of Neurology (N.R.P., A.L.C., M.H.M., I.R.C., R.B., E.J.G., N.H.P., E.E.L., A.d.H., K.N.S.), and Department of Radiology (S.P., G.S.), Yale University School of Medicine, New Haven, CT; Computer Science and Artificial Intelligence Laboratory (CSAIL) (J.E.I.), Massachusetts Institute of Technology, Cambridge; Athinoula A. Martinos Center for Biomedical Imaging (J.E.I., M.S.R.), Massachusetts General Hospital, Charlestown; Department of Neurosurgery (S.B.O., C.M.), Yale University School of Medicine, New Haven, CT; Department of Neurosurgery (S.J.S.), Engineering Science and Mechanics, and Physics, The Pennsylvania State University, University Park; and Division of Neurocritical Care (W.T.K.), Department of Neurology, Massachusetts General Hospital, Boston
| | - Erin E Longbrake
- From the Department of Neurology (N.R.P., A.L.C., M.H.M., I.R.C., R.B., E.J.G., N.H.P., E.E.L., A.d.H., K.N.S.), and Department of Radiology (S.P., G.S.), Yale University School of Medicine, New Haven, CT; Computer Science and Artificial Intelligence Laboratory (CSAIL) (J.E.I.), Massachusetts Institute of Technology, Cambridge; Athinoula A. Martinos Center for Biomedical Imaging (J.E.I., M.S.R.), Massachusetts General Hospital, Charlestown; Department of Neurosurgery (S.B.O., C.M.), Yale University School of Medicine, New Haven, CT; Department of Neurosurgery (S.J.S.), Engineering Science and Mechanics, and Physics, The Pennsylvania State University, University Park; and Division of Neurocritical Care (W.T.K.), Department of Neurology, Massachusetts General Hospital, Boston
| | - Adam de Havenon
- From the Department of Neurology (N.R.P., A.L.C., M.H.M., I.R.C., R.B., E.J.G., N.H.P., E.E.L., A.d.H., K.N.S.), and Department of Radiology (S.P., G.S.), Yale University School of Medicine, New Haven, CT; Computer Science and Artificial Intelligence Laboratory (CSAIL) (J.E.I.), Massachusetts Institute of Technology, Cambridge; Athinoula A. Martinos Center for Biomedical Imaging (J.E.I., M.S.R.), Massachusetts General Hospital, Charlestown; Department of Neurosurgery (S.B.O., C.M.), Yale University School of Medicine, New Haven, CT; Department of Neurosurgery (S.J.S.), Engineering Science and Mechanics, and Physics, The Pennsylvania State University, University Park; and Division of Neurocritical Care (W.T.K.), Department of Neurology, Massachusetts General Hospital, Boston
| | - Steven J Schiff
- From the Department of Neurology (N.R.P., A.L.C., M.H.M., I.R.C., R.B., E.J.G., N.H.P., E.E.L., A.d.H., K.N.S.), and Department of Radiology (S.P., G.S.), Yale University School of Medicine, New Haven, CT; Computer Science and Artificial Intelligence Laboratory (CSAIL) (J.E.I.), Massachusetts Institute of Technology, Cambridge; Athinoula A. Martinos Center for Biomedical Imaging (J.E.I., M.S.R.), Massachusetts General Hospital, Charlestown; Department of Neurosurgery (S.B.O., C.M.), Yale University School of Medicine, New Haven, CT; Department of Neurosurgery (S.J.S.), Engineering Science and Mechanics, and Physics, The Pennsylvania State University, University Park; and Division of Neurocritical Care (W.T.K.), Department of Neurology, Massachusetts General Hospital, Boston
| | - Matthew S Rosen
- From the Department of Neurology (N.R.P., A.L.C., M.H.M., I.R.C., R.B., E.J.G., N.H.P., E.E.L., A.d.H., K.N.S.), and Department of Radiology (S.P., G.S.), Yale University School of Medicine, New Haven, CT; Computer Science and Artificial Intelligence Laboratory (CSAIL) (J.E.I.), Massachusetts Institute of Technology, Cambridge; Athinoula A. Martinos Center for Biomedical Imaging (J.E.I., M.S.R.), Massachusetts General Hospital, Charlestown; Department of Neurosurgery (S.B.O., C.M.), Yale University School of Medicine, New Haven, CT; Department of Neurosurgery (S.J.S.), Engineering Science and Mechanics, and Physics, The Pennsylvania State University, University Park; and Division of Neurocritical Care (W.T.K.), Department of Neurology, Massachusetts General Hospital, Boston
| | - W Taylor Kimberly
- From the Department of Neurology (N.R.P., A.L.C., M.H.M., I.R.C., R.B., E.J.G., N.H.P., E.E.L., A.d.H., K.N.S.), and Department of Radiology (S.P., G.S.), Yale University School of Medicine, New Haven, CT; Computer Science and Artificial Intelligence Laboratory (CSAIL) (J.E.I.), Massachusetts Institute of Technology, Cambridge; Athinoula A. Martinos Center for Biomedical Imaging (J.E.I., M.S.R.), Massachusetts General Hospital, Charlestown; Department of Neurosurgery (S.B.O., C.M.), Yale University School of Medicine, New Haven, CT; Department of Neurosurgery (S.J.S.), Engineering Science and Mechanics, and Physics, The Pennsylvania State University, University Park; and Division of Neurocritical Care (W.T.K.), Department of Neurology, Massachusetts General Hospital, Boston
| | - Kevin N Sheth
- From the Department of Neurology (N.R.P., A.L.C., M.H.M., I.R.C., R.B., E.J.G., N.H.P., E.E.L., A.d.H., K.N.S.), and Department of Radiology (S.P., G.S.), Yale University School of Medicine, New Haven, CT; Computer Science and Artificial Intelligence Laboratory (CSAIL) (J.E.I.), Massachusetts Institute of Technology, Cambridge; Athinoula A. Martinos Center for Biomedical Imaging (J.E.I., M.S.R.), Massachusetts General Hospital, Charlestown; Department of Neurosurgery (S.B.O., C.M.), Yale University School of Medicine, New Haven, CT; Department of Neurosurgery (S.J.S.), Engineering Science and Mechanics, and Physics, The Pennsylvania State University, University Park; and Division of Neurocritical Care (W.T.K.), Department of Neurology, Massachusetts General Hospital, Boston.
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16
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Webb A, O'Reilly T. Tackling SNR at low-field: a review of hardware approaches for point-of-care systems. MAGMA (NEW YORK, N.Y.) 2023:10.1007/s10334-023-01100-3. [PMID: 37202656 PMCID: PMC10386948 DOI: 10.1007/s10334-023-01100-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 05/03/2023] [Accepted: 05/09/2023] [Indexed: 05/20/2023]
Abstract
OBJECTIVE To review the major hardware components of low-field point-of-care MRI systems which affect the overall sensitivity. METHODS Designs for the following components are reviewed and analyzed: magnet, RF coils, transmit/receive switches, preamplifiers, data acquisition system, and methods for grounding and mitigating electromagnetic interference. RESULTS High homogeneity magnets can be produced in a variety of different designs including C- and H-shaped as well as Halbach arrays. Using Litz wire for RF coil designs enables unloaded Q values of ~ 400 to be reached, with body loss representing about 35% of the total system resistance. There are a number of different schemes to tackle issues arising from the low coil bandwidth with respect to the imaging bandwidth. Finally, the effects of good RF shielding, proper electrical grounding, and effective electromagnetic interference reduction can lead to substantial increases in image signal-to-noise ratio. DISCUSSION There are many different magnet and RF coil designs in the literature, and to enable meaningful comparisons and optimizations to be performed it would be very helpful to determine a standardized set of sensitivity measures, irrespective of design.
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Affiliation(s)
- Andrew Webb
- Department of Radiology, C.J. Gorter MRI Center, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands.
| | - Thomas O'Reilly
- Department of Radiology, C.J. Gorter MRI Center, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
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17
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Petersen NH, Sheth KN, Jha RM. Precision Medicine in Neurocritical Care for Cerebrovascular Disease Cases. Stroke 2023; 54:1392-1402. [PMID: 36789774 PMCID: PMC10348371 DOI: 10.1161/strokeaha.122.036402] [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: 04/16/2022] [Accepted: 12/22/2022] [Indexed: 02/16/2023]
Abstract
Scientific advances have informed many aspects of acute stroke care but have also highlighted the complexity and heterogeneity of cerebrovascular diseases. While practice guidelines are essential in supporting the clinical decision-making process, they may not capture the nuances of individual cases. Personalized stroke care in ICU has traditionally relied on integrating clinical examinations, neuroimaging studies, and physiologic monitoring to develop a treatment plan tailored to the individual patient. However, to realize the potential of precision medicine in stroke, we need advances and evidence in several critical areas, including data capture, clinical phenotyping, serum biomarker development, neuromonitoring, and physiology-based treatment targets. Mathematical tools are being developed to analyze the multitude of data and provide clinicians with real-time information and personalized treatment targets for the critical care management of patients with cerebrovascular diseases. This review summarizes research advances in these areas and outlines principles for translating precision medicine into clinical practice.
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Affiliation(s)
- Nils H Petersen
- Departments of Neurology (N.H.P., K.N.S., R.M.J.), Yale University School of Medicine, New Haven, CT
| | - Kevin N Sheth
- Departments of Neurology (N.H.P., K.N.S., R.M.J.), Yale University School of Medicine, New Haven, CT
- Neurosurgery (K.N.S., R.M.J.), Yale University School of Medicine, New Haven, CT
- Departments of Neurology, Neurosurgery and Translational Neuroscience, Barrow Neurological Institute, Phoenix, AZ (K.N.S., R.M.J.)
| | - Ruchira M Jha
- Departments of Neurology (N.H.P., K.N.S., R.M.J.), Yale University School of Medicine, New Haven, CT
- Neurosurgery (K.N.S., R.M.J.), Yale University School of Medicine, New Haven, CT
- Departments of Neurology, Neurosurgery and Translational Neuroscience, Barrow Neurological Institute, Phoenix, AZ (K.N.S., R.M.J.)
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18
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Zirpe KG, Alunpipatthanachai B, Matin N, Gulek BG, Blissitt PA, Palmieri K, Rosenblatt K, Athiraman U, Gollapudy S, Theard MA, Wahlster S, Vavilala MS, Lele AV. Benchmarking Hospital Practices and Policies on Intrahospital Neurocritical Care Transport: The Safe-Neuro-Transport Study. J Clin Med 2023; 12:jcm12093183. [PMID: 37176625 PMCID: PMC10179223 DOI: 10.3390/jcm12093183] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 04/07/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023] Open
Abstract
An electronic survey was administered to multidisciplinary neurocritical care providers at 365 hospitals in 32 countries to describe intrahospital transport (IHT) practices of neurocritically ill patients at their institutions. The reported IHT practices were stratified by World Bank country income level. Variability between high-income (HIC) and low/middle-income (LMIC) groups, as well as variability between hospitals within countries, were expressed as counts/percentages and intracluster correlation coefficients (ICCs) with a 95% confidence interval (CI). A total of 246 hospitals (67% response rate; n = 103, 42% HIC and n = 143, 58% LMIC) participated. LMIC hospitals were less likely to report a portable CT scanner (RR 0.39, 95% CI [0.23; 0.67]), more likely to report a pre-IHT checklist (RR 2.18, 95% CI [1.53; 3.11]), and more likely to report that intensive care unit (ICU) physicians routinely participated in IHTs (RR 1.33, 95% CI [1.02; 1.72]). Between- and across-country variation were highest for pre-IHT external ventricular drain clamp tolerance (reported by 40% of the hospitals, ICC 0.22, 95% CI 0.00-0.46) and end-tidal carbon dioxide monitoring during IHT (reported by 29% of the hospitals, ICC 0.46, 95% CI 0.07-0.71). Brain tissue oxygenation monitoring during IHT was reported by only 9% of the participating hospitals. An IHT standard operating procedure (SOP)/hospital policy (HP) was reported by 37% (n = 90); HIC: 43% (n= 44) vs. LMIC: 32% (n = 46), p = 0.56. Amongst the IHT SOP/HPs reviewed (n = 13), 90% did not address the continuation of hemodynamic and neurophysiological monitoring during IHT. In conclusion, the development of a neurocritical-care-specific IHT SOP/HP as well as the alignment of practices related to the IHT of neurocritically ill patients are urgent unmet needs. Inconsistent standards related to neurophysiological monitoring during IHT warrant in-depth scrutiny across hospitals and suggest a need for international guidelines for neurocritical care IHT.
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Affiliation(s)
- Kapil G Zirpe
- Neurotrauma Unit, Ruby Hall Clinic, Pune 411040, India
| | | | - Nassim Matin
- Neurocritical Care Service, Department of Neurology, Harborview Medical Center, University of Washington, Seattle, WA 98104, USA
| | - Bernice G Gulek
- Neurocritical Care Service, Department of Neurology, Harborview Medical Center, University of Washington, Seattle, WA 98104, USA
| | - Patricia A Blissitt
- Harborview Medical Center, University of Washington School of Nursing, Seattle, WA 98104, USA
| | - Katherine Palmieri
- Department of Anesthesiology, University of Kansas Health System, Kansas City, KS 66160, USA
| | - Kathryn Rosenblatt
- Department of Anesthesiology, Johns Hopkins Medical Institutions, Baltimore, MD 21287, USA
| | | | | | - Marie Angele Theard
- Department of Anesthesiology and Pain Medicine, Harborview Injury Prevention and Research Center, Harborview Medical Center, University of Washington, Seattle, WA 98122, USA
| | - Sarah Wahlster
- Neurocritical Care Service, Department of Neurology, Harborview Medical Center, University of Washington, Seattle, WA 98104, USA
- Neurocritical Care Service, Department of Anesthesiology and Pain Medicine, Harborview Injury Prevention and Research Center, Harborview Medical Center, University of Washington, Seattle, WA 98122, USA
| | - Monica S Vavilala
- Department of Anesthesiology and Pain Medicine, Harborview Injury Prevention and Research Center, Harborview Medical Center, University of Washington, Seattle, WA 98122, USA
| | - Abhijit V Lele
- Neurocritical Care Service, Department of Anesthesiology and Pain Medicine, Harborview Injury Prevention and Research Center, Harborview Medical Center, University of Washington, Seattle, WA 98122, USA
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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: 72] [Impact Index Per Article: 72.0] [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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22
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Yuen MM, Prabhat AM, Mazurek MH, Chavva IR, Crawford A, Cahn BA, Beekman R, Kim JA, Gobeske KT, Petersen NH, Falcone GJ, Gilmore EJ, Hwang DY, Jasne AS, Amin H, Sharma R, Matouk C, Ward A, Schindler J, Sansing L, de Havenon A, Aydin A, Wira C, Sze G, Rosen MS, Kimberly WT, Sheth KN. Portable, low-field magnetic resonance imaging enables highly accessible and dynamic bedside evaluation of ischemic stroke. SCIENCE ADVANCES 2022; 8:eabm3952. [PMID: 35442729 PMCID: PMC9020661 DOI: 10.1126/sciadv.abm3952] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 02/08/2022] [Indexed: 05/26/2023]
Abstract
Brain imaging is essential to the clinical management of patients with ischemic stroke. Timely and accessible neuroimaging, however, can be limited in clinical stroke pathways. Here, portable magnetic resonance imaging (pMRI) acquired at very low magnetic field strength (0.064 T) is used to obtain actionable bedside neuroimaging for 50 confirmed patients with ischemic stroke. Low-field pMRI detected infarcts in 45 (90%) patients across cortical, subcortical, and cerebellar structures. Lesions as small as 4 mm were captured. Infarcts appeared as hyperintense regions on T2-weighted, fluid-attenuated inversion recovery and diffusion-weighted imaging sequences. Stroke volume measurements were consistent across pMRI sequences and between low-field pMRI and conventional high-field MRI studies. Low-field pMRI stroke volumes significantly correlated with stroke severity and functional outcome at discharge. These results validate the use of low-field pMRI to obtain clinically useful imaging of stroke, setting the stage for use in resource-limited environments.
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Affiliation(s)
- Matthew M. Yuen
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Anjali M. Prabhat
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Mercy H. Mazurek
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Isha R. Chavva
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Anna Crawford
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Bradley A. Cahn
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Rachel Beekman
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Jennifer A. Kim
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Kevin T. Gobeske
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Nils H. Petersen
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Guido J. Falcone
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Emily J. Gilmore
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - David Y. Hwang
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Adam S. Jasne
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Hardik Amin
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Richa Sharma
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Charles Matouk
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Adrienne Ward
- Neuroscience Intensive Care Unit, Yale New Haven Hospital, New Haven, CT, USA
| | - Joseph Schindler
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Lauren Sansing
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Adam de Havenon
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Ani Aydin
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Charles Wira
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Gordon Sze
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Matthew S. Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - W. Taylor Kimberly
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Kevin N. Sheth
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
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