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Kung HT, Cui SX, Kaplan JT, Joshi AA, Leahy RM, Nayak KS, Haldar JP. Diffusion tensor brain imaging at 0.55T: A feasibility study. Magn Reson Med 2024; 92:1649-1657. [PMID: 38725132 DOI: 10.1002/mrm.30156] [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: 11/06/2023] [Revised: 04/09/2024] [Accepted: 04/28/2024] [Indexed: 07/23/2024]
Abstract
PURPOSE To investigate the feasibility of diffusion tensor brain imaging at 0.55T with comparisons against 3T. METHODS Diffusion tensor imaging data with 2 mm isotropic resolution was acquired on a cohort of five healthy subjects using both 0.55T and 3T scanners. The signal-to-noise ratio (SNR) of the 0.55T data was improved using a previous SNR-enhancing joint reconstruction method that jointly reconstructs the entire set of diffusion weighted images from k-space using shared-edge constraints. Quantitative diffusion tensor parameters were estimated and compared across field strengths. We also performed a test-retest assessment of repeatability at each field strength. RESULTS After applying SNR-enhancing joint reconstruction, the diffusion tensor parameters obtained from 0.55T data were strongly correlated (R 2 ≥ 0 . 70 $$ {R}^2\ge 0.70 $$ ) with those obtained from 3T data. Test-retest analysis showed that SNR-enhancing reconstruction improved the repeatability of the 0.55T diffusion tensor parameters. CONCLUSION High-resolution in vivo diffusion MRI of the human brain is feasible at 0.55T when appropriate noise-mitigation strategies are applied.
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Affiliation(s)
- Hao-Ting Kung
- Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA
| | - Sophia X Cui
- Siemens Medical Solutions USA, Los Angeles, California, USA
| | - Jonas T Kaplan
- Brain and Creativity Institute, University of Southern California, Los Angeles, California, USA
| | - Anand A Joshi
- Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA
| | - Richard M Leahy
- Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA
| | - Krishna S Nayak
- Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA
| | - Justin P Haldar
- Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA
- Brain and Creativity Institute, University of Southern California, Los Angeles, California, USA
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2
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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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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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Su S, Zhao Y, Ding Y, Lau V, Xiao L, Leung GKK, Lau GKK, Huang F, Vardhanabhuti V, Leong ATL, Wu EX. Ultra-low-field magnetization transfer imaging at 0.055T with low specific absorption rate. Magn Reson Med 2024. [PMID: 39044654 DOI: 10.1002/mrm.30231] [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: 06/23/2023] [Revised: 06/14/2024] [Accepted: 07/09/2024] [Indexed: 07/25/2024]
Abstract
PURPOSE To demonstrate magnetization transfer (MT) effects with low specific absorption rate (SAR) on ultra-low-field (ULF) MRI. METHODS MT imaging was implemented by using sinc-modulated RF pulse train (SPT) modules to provide bilateral off-resonance irradiation. They were incorporated into 3D gradient echo (GRE) and fast spin echo (FSE) protocols on a shielding-free 0.055T head scanner. MT effects were first verified using phantoms. Brain MT imaging was conducted in both healthy subjects and patients. RESULTS MT effects were clearly observed in phantoms using six SPT modules with total flip angle 3600° at central primary saturation bands of approximate offset ±786 Hz, even in the presence of large relative B0 inhomogeneity. For brain, strong MT effects were observed in gray matter, white matter, and muscle in 3D GRE and FSE imaging using six and sixteen SPT modules with total flip angle 3600° and 9600°, respectively. Fat, cerebrospinal fluid, and blood exhibited relatively weak MT effects. MT preparation enhanced tissue contrasts in T2-weighted and FLAIR-like images, and improved brain lesion delineation. The estimated MT SAR was 0.0024 and 0.0008 W/kg for two protocols, respectively, which is far below the US Food and Drug Administration (FDA) limit of 3.0 W/kg. CONCLUSION Robust MT effects can be readily obtained at ULF with extremely low SAR, despite poor relative B0 homogeneity in ppm. This unique advantage enables flexible MT pulse design and implementation on low-cost ULF MRI platforms to achieve strong MT effects in brain and beyond, potentially augmenting their clinical utility in the future.
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Affiliation(s)
- Shi Su
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Yujiao Zhao
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Ye Ding
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Vick Lau
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Linfang Xiao
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Gilberto K K Leung
- Department of Surgery, The University of Hong Kong, Hong Kong SAR, China
| | - Gary K K Lau
- Department of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Fan Huang
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong SAR, China
| | - Vince Vardhanabhuti
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong SAR, China
| | - Alex T L Leong
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Ed X Wu
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
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Tu LH, Tegtmeyer K, de Oliveira Santo ID, Venkatesh AK, Forman HP, Mahajan A, Melnick ER. Abbreviated MRI in the evaluation of dizziness: report turnaround times and impact on length of stay compared to CT, CTA, and conventional MRI. Emerg Radiol 2024:10.1007/s10140-024-02273-7. [PMID: 39034381 DOI: 10.1007/s10140-024-02273-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2024] [Accepted: 07/16/2024] [Indexed: 07/23/2024]
Abstract
PURPOSE Neuroimaging is often used in the emergency department (ED) to evaluate for posterior circulation strokes in patients with dizziness, commonly with CT/CTA due to speed and availability. Although MRI offers more sensitive evaluation, it is less commonly used, in part due to slower turnaround times. We assess the potential for abbreviated MRI to improve reporting times and impact on length of stay (LOS) compared to conventional MRI (as well as CT/CTA) in the evaluation of acute dizziness. MATERIALS AND METHODS We performed a retrospective analysis of length of stay via LASSO regression for patients presenting to the ED with dizziness and discharged directly from the ED over 4 years (1/1/2018-12/31/2021), controlling for numerous patient-level and logistical factors. We additionally assessed turnaround time between order and final report for various imaging modalities. RESULTS 14,204 patients were included in our analysis. Turnaround time for abbreviated MRI was significantly lower than for conventional MRI (4.40 h vs. 6.14 h, p < 0.001) with decreased impact on LOS (0.58 h vs. 2.02 h). Abbreviated MRI studies had longer turnaround time (4.40 h vs. 1.41 h, p < 0.001) and was associated with greater impact on ED LOS than non-contrast CT head (0.58 h vs. 0.00 h), however there was no significant difference in turnaround time compared to CTA head and neck (4.40 h vs. 3.86 h, p = 0.06) with similar effect on LOS (0.58 h vs. 0.53 h). Ordering both CTA and conventional MRI was associated with a greater-than-linear increase in LOS (additional 0.37 h); the same trend was not seen combining CTA and abbreviated MRI (additional 0.00 h). CONCLUSIONS In the acute settings where MRI is available, abbreviated MRI protocols may improve turnaround times and LOS compared to conventional MRI protocols. Since recent guidelines recommend MRI over CT in the evaluation of dizziness, implementation of abbreviated MRI protocols has the potential to facilitate rapid access to preferred imaging, while minimizing impact on ED workflows.
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Affiliation(s)
- Long H Tu
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Tompkins East 2, New Haven, CT 06520, USA.
| | - Kyle Tegtmeyer
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Tompkins East 2, New Haven, CT 06520, USA
| | - Irene Dixe de Oliveira Santo
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Tompkins East 2, New Haven, CT 06520, USA
| | - Arjun K Venkatesh
- Department of Emergency Medicine, Yale School of Medicine, 464 Congress Ave # 260, New Haven, CT 06519, USA
| | - Howard P Forman
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Tompkins East 2, New Haven, CT 06520, USA
| | - Amit Mahajan
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Tompkins East 2, New Haven, CT 06520, USA
| | - Edward R Melnick
- Department of Emergency Medicine, Yale School of Medicine, 464 Congress Ave # 260, New Haven, CT 06519, USA
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Su S, Hu J, Ding Y, Zhang J, Lau V, Zhao Y, Wu EX. Ultra-low-field magnetic resonance angiography at 0.05 T: A preliminary study. NMR IN BIOMEDICINE 2024:e5213. [PMID: 39032076 DOI: 10.1002/nbm.5213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 05/24/2024] [Accepted: 06/18/2024] [Indexed: 07/22/2024]
Abstract
We aim to explore the feasibility of head and neck time-of-flight (TOF) magnetic resonance angiography (MRA) at ultra-low-field (ULF). TOF MRA was conducted on a highly simplified 0.05 T MRI scanner with no radiofrequency (RF) and magnetic shielding. A flow-compensated three-dimensional (3D) gradient echo (GRE) sequence with a tilt-optimized nonsaturated excitation RF pulse, and a flow-compensated multislice two-dimensional (2D) GRE sequence, were implemented for cerebral artery and vein imaging, respectively. For carotid artery and jugular vein imaging, flow-compensated 2D GRE sequences were utilized with venous and arterial blood presaturation, respectively. MRA was performed on young healthy subjects. Vessel-to-background contrast was experimentally observed with strong blood inflow effect and background tissue suppression. The large primary cerebral arteries and veins, carotid arteries, jugular veins, and artery bifurcations could be identified in both raw GRE images and maximum intensity projections. The primary brain and neck arteries were found to be reproducible among multiple examination sessions. These preliminary experimental results demonstrated the possibility of artery TOF MRA on low-cost 0.05 T scanners for the first time, despite the extremely low MR signal. We expect to improve the quality of ULF TOF MRA in the near future through sequence development and optimization, ongoing advances in ULF hardware and image formation, and the use of vascular T1 contrast agents.
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Affiliation(s)
- Shi Su
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Jiahao Hu
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Ye Ding
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Junhao Zhang
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Vick Lau
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Yujiao Zhao
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Ed X Wu
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China
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7
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Lee H, Lee J, Jung D, Oh H, Shin H, Choi B. Neuroprotection of Transcranial Cortical and Peripheral Somatosensory Electrical Stimulation by Modulating a Common Neuronal Death Pathway in Mice with Ischemic Stroke. Int J Mol Sci 2024; 25:7546. [PMID: 39062789 PMCID: PMC11277498 DOI: 10.3390/ijms25147546] [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: 06/18/2024] [Revised: 07/05/2024] [Accepted: 07/08/2024] [Indexed: 07/28/2024] Open
Abstract
Therapeutic electrical stimulation, such as transcranial cortical stimulation and peripheral somatosensory stimulation, is used to improve motor function in patients with stroke. We hypothesized that these stimulations exert neuroprotective effects during the subacute phase of ischemic stroke by regulating novel common signaling pathways. Male C57BL/6J mouse models of ischemic stroke were treated with high-definition (HD)-transcranial alternating current stimulation (tACS; 20 Hz, 89.1 A/mm2), HD-transcranial direct current stimulation (tDCS; intensity, 55 A/mm2; charge density, 66,000 C/m2), or electroacupuncture (EA, 2 Hz, 1 mA) in the early stages of stroke. The therapeutic effects were assessed using behavioral motor function tests. The underlying mechanisms were determined using transcriptomic and other biomedical analyses. All therapeutic electrical tools alleviated the motor dysfunction caused by ischemic stroke insults. We focused on electrically stimulating common genes involved in apoptosis and cell death using transcriptome analysis and chose 11 of the most potent targets (Trem2, S100a9, Lgals3, Tlr4, Myd88, NF-kB, STAT1, IL-6, IL-1β, TNF-α, and Iba1). Subsequent investigations revealed that electrical stimulation modulated inflammatory cytokines, including IL-1β and TNF-α, by regulating STAT1 and NF-kB activation, especially in amoeboid microglia; moreover, electrical stimulation enhanced neuronal survival by activating neurotrophic factors, including BDNF and FGF9. Therapeutic electrical stimulation applied to the transcranial cortical- or periphery-nerve level to promote functional recovery may improve neuroprotection by modulating a common neuronal death pathway and upregulating neurotrophic factors. Therefore, combining transcranial cortical and peripheral somatosensory stimulation may exert a synergistic neuroprotective effect, further enhancing the beneficial effects on motor deficits in patients with ischemic stroke.
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Affiliation(s)
- Hongju Lee
- Department of Korean Medical Science, School of Korean Medicine, Pusan National University, Yangsan 50612, Republic of Korea; (H.L.); (J.L.); (D.J.); (H.O.); (H.S.)
| | - Juyeon Lee
- Department of Korean Medical Science, School of Korean Medicine, Pusan National University, Yangsan 50612, Republic of Korea; (H.L.); (J.L.); (D.J.); (H.O.); (H.S.)
- Graduate Training Program of Korean Medical Therapeutics for Healthy Aging, Pusan National University, Yangsan 50612, Republic of Korea
| | - Dahee Jung
- Department of Korean Medical Science, School of Korean Medicine, Pusan National University, Yangsan 50612, Republic of Korea; (H.L.); (J.L.); (D.J.); (H.O.); (H.S.)
- Graduate Training Program of Korean Medical Therapeutics for Healthy Aging, Pusan National University, Yangsan 50612, Republic of Korea
| | - Harim Oh
- Department of Korean Medical Science, School of Korean Medicine, Pusan National University, Yangsan 50612, Republic of Korea; (H.L.); (J.L.); (D.J.); (H.O.); (H.S.)
- Graduate Training Program of Korean Medical Therapeutics for Healthy Aging, Pusan National University, Yangsan 50612, Republic of Korea
| | - Hwakyoung Shin
- Department of Korean Medical Science, School of Korean Medicine, Pusan National University, Yangsan 50612, Republic of Korea; (H.L.); (J.L.); (D.J.); (H.O.); (H.S.)
- Graduate Training Program of Korean Medical Therapeutics for Healthy Aging, Pusan National University, Yangsan 50612, Republic of Korea
| | - Byungtae Choi
- Department of Korean Medical Science, School of Korean Medicine, Pusan National University, Yangsan 50612, Republic of Korea; (H.L.); (J.L.); (D.J.); (H.O.); (H.S.)
- Graduate Training Program of Korean Medical Therapeutics for Healthy Aging, Pusan National University, Yangsan 50612, Republic of Korea
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8
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Murali S, Ding H, Adedeji F, Qin C, Obungoloch J, Asllani I, Anazodo U, Ntusi NAB, Mammen R, Niendorf T, Adeleke S. Bringing MRI to low- and middle-income countries: Directions, challenges and potential solutions. NMR IN BIOMEDICINE 2024; 37:e4992. [PMID: 37401341 DOI: 10.1002/nbm.4992] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 05/27/2023] [Accepted: 05/30/2023] [Indexed: 07/05/2023]
Abstract
The global disparity of magnetic resonance imaging (MRI) is a major challenge, with many low- and middle-income countries (LMICs) experiencing limited access to MRI. The reasons for limited access are technological, economic and social. With the advancement of MRI technology, we explore why these challenges still prevail, highlighting the importance of MRI as the epidemiology of disease changes in LMICs. In this paper, we establish a framework to develop MRI with these challenges in mind and discuss the different aspects of MRI development, including maximising image quality using cost-effective components, integrating local technology and infrastructure and implementing sustainable practices. We also highlight the current solutions-including teleradiology, artificial intelligence and doctor and patient education strategies-and how these might be further improved to achieve greater access to MRI.
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Affiliation(s)
- Sanjana Murali
- School of Medicine, Faculty of Medicine, Imperial College London, London, UK
| | - Hao Ding
- School of Medicine, Faculty of Medicine, Imperial College London, London, UK
| | - Fope Adedeji
- School of Medicine, Faculty of Medicine, University College London, London, UK
| | - Cathy Qin
- Department of Imaging, Imperial College Healthcare NHS Trust, London, UK
| | - Johnes Obungoloch
- Department of Biomedical Engineering, Mbarara University of Science and Technology, Mbarara, Uganda
| | - Iris Asllani
- Department of Biomedical Engineering, Rochester Institute of Technology, Rochester, New York, USA
| | - Udunna Anazodo
- Department of Medical Biophysics, Western University, London, Ontario, Canada
- The Research Institute of London Health Sciences Centre and St. Joseph's Health Care, London, Ontario, Canada
| | - Ntobeko A B Ntusi
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
- South African Medical Research Council Extramural Unit on Intersection of Noncommunicable Diseases and Infectious Diseases, Cape Town, South Africa
| | - Regina Mammen
- Department of Cardiology, The Essex Cardiothoracic Centre, Basildon, UK
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility (BUFF), Max-Delbrück Centre for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Sola Adeleke
- School of Cancer & Pharmaceutical Sciences, King's College London, London, UK
- High Dimensional Neuro-oncology, University College London Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
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9
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Zhao Y, Xiao L, Hu J, Wu EX. Robust EMI elimination for RF shielding-free MRI through deep learning direct MR signal prediction. Magn Reson Med 2024; 92:112-127. [PMID: 38376455 DOI: 10.1002/mrm.30046] [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: 05/08/2023] [Revised: 01/23/2024] [Accepted: 01/23/2024] [Indexed: 02/21/2024]
Abstract
PURPOSE To develop a new electromagnetic interference (EMI) elimination strategy for RF shielding-free MRI via active EMI sensing and deep learning direct MR signal prediction (Deep-DSP). METHODS Deep-DSP is proposed to directly predict EMI-free MR signals. During scanning, MRI receive coil and EMI sensing coils simultaneously sample data within two windows (i.e., for MR data and EMI characterization data acquisition, respectively). Afterward, a residual U-Net model is trained using synthetic MRI receive coil data and EMI sensing coil data acquired during EMI signal characterization window, to predict EMI-free MR signals from signals acquired by MRI receive and EMI sensing coils. The trained model is then used to directly predict EMI-free MR signals from data acquired by MRI receive and sensing coils during the MR signal-acquisition window. This strategy was evaluated on an ultralow-field 0.055T brain MRI scanner without any RF shielding and a 1.5T whole-body scanner with incomplete RF shielding. RESULTS Deep-DSP accurately predicted EMI-free MR signals in presence of strong EMI. It outperformed recently developed EDITER and convolutional neural network methods, yielding better EMI elimination and enabling use of few EMI sensing coils. Furthermore, it could work well without dedicated EMI characterization data. CONCLUSION Deep-DSP presents an effective EMI elimination strategy that outperforms existing methods, advancing toward truly portable and patient-friendly MRI. It exploits electromagnetic coupling between MRI receive and EMI sensing coils as well as typical MR signal characteristics. Despite its deep learning nature, Deep-DSP framework is computationally simple and efficient.
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Affiliation(s)
- Yujiao Zhao
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, People's Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Linfang Xiao
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, People's Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Jiahao Hu
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, People's Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Ed X Wu
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, People's Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, People's Republic of China
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10
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Samardzija A, Selvaganesan K, Zhang HZ, Sun H, Sun C, Ha Y, Galiana G, Constable RT. Low-Field, Low-Cost, Point-of-Care Magnetic Resonance Imaging. Annu Rev Biomed Eng 2024; 26:67-91. [PMID: 38211326 DOI: 10.1146/annurev-bioeng-110122-022903] [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: 01/13/2024]
Abstract
Low-field magnetic resonance imaging (MRI) has recently experienced a renaissance that is largely attributable to the numerous technological advancements made in MRI, including optimized pulse sequences, parallel receive and compressed sensing, improved calibrations and reconstruction algorithms, and the adoption of machine learning for image postprocessing. This new attention on low-field MRI originates from a lack of accessibility to traditional MRI and the need for affordable imaging. Low-field MRI provides a viable option due to its lack of reliance on radio-frequency shielding rooms, expensive liquid helium, and cryogen quench pipes. Moreover, its relatively small size and weight allow for easy and affordable installation in most settings. Rather than replacing conventional MRI, low-field MRI will provide new opportunities for imaging both in developing and developed countries. This article discusses the history of low-field MRI, low-field MRI hardware and software, current devices on the market, advantages and disadvantages, and low-field MRI's global potential.
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Affiliation(s)
- Anja Samardzija
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA;
| | - Kartiga Selvaganesan
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA;
| | - Horace Z Zhang
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA;
| | - Heng Sun
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA;
| | - Chenhao Sun
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Yonghyun Ha
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Gigi Galiana
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA;
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - R Todd Constable
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA;
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
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11
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Zhao Y, Xiao L, Liu Y, Leong AT, Wu EX. Electromagnetic interference elimination via active sensing and deep learning prediction for radiofrequency shielding-free MRI. NMR IN BIOMEDICINE 2024; 37:e4956. [PMID: 37088894 DOI: 10.1002/nbm.4956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 04/12/2023] [Accepted: 04/19/2023] [Indexed: 05/03/2023]
Abstract
At present, MRI scans are typically performed inside fully enclosed radiofrequency (RF) shielding rooms, posing stringent installation requirements and causing patient discomfort. We aim to eliminate electromagnetic interference (EMI) for MRI with no or incomplete RF shielding. In this study, a method of active sensing and deep learning EMI prediction is presented to model, predict, and remove EMI signal components from acquired MRI signals. Specifically, during each MRI scan, separate EMI-sensing coils placed in various locations are utilized to simultaneously sample external and internal EMI signals within two windows (for both conventional MRI signal acquisition and EMI characterization acquisition). A convolution neural network model is trained using the EMI characterization data to relate EMI signals detected by EMI-sensing coils to EMI signals in the MRI receive coil. This model is then used to retrospectively predict and remove EMI signal components detected by the MRI receive coil during the MRI signal acquisition window. This strategy was implemented on a low-cost ultralow-field 0.055 T permanent magnet MRI scanner without RF shielding. It produced final image signal-to-noise ratios that were comparable with those obtained using a fully enclosed RF shielding cage, and outperformed existing analytical EMI elimination methods (i.e., spectral domain transfer function and external dynamic interference estimation and removal [EDITER] methods). A preliminary experiment also demonstrated its applicability on a 1.5 T superconducting magnet MRI scanner with incomplete RF shielding. Altogether, the results demonstrated that the proposed method was highly effective in predicting and removing various EMI signals from both external environments and internal scanner electronics at both 0.055 T (2.3 MHz) and 1.5 T (63.9 MHz). The proposed strategy enables shielding-free MRI. The concept is relatively simple and is potentially applicable to other RF signal detection scenarios in the presence of external and/or internal EMI.
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Affiliation(s)
- Yujiao Zhao
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, SAR, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, SAR, China
| | - Linfang Xiao
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, SAR, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, SAR, China
| | - Yilong Liu
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, SAR, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, SAR, China
| | - Alex T Leong
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, SAR, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, SAR, China
| | - Ed X Wu
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, SAR, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, SAR, China
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12
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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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13
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Iqbal N, Brittin DO, Daluwathumullagamage PJ, Alam MS, Senanayake IM, Gafar AT, Siraj Z, Petrilla A, Pugh M, Tonazzi B, Ragunathan S, Poorman ME, Sacolick L, Theis T, Rosen MS, Chekmenev EY, Goodson BM. Toward Next-Generation Molecular Imaging with a Clinical Low-Field (0.064 T) Point-of-Care MRI Scanner. Anal Chem 2024; 96:10348-10355. [PMID: 38857182 DOI: 10.1021/acs.analchem.4c01299] [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] [Indexed: 06/12/2024]
Abstract
Low-field (LF) MRI promises soft-tissue imaging without the expensive, immobile magnets of clinical scanners but generally suffers from limited detection sensitivity and contrast. The sensitivity boost provided by hyperpolarization can thus be highly synergistic with LF MRI. Initial efforts to integrate a continuous-bubbling SABRE (signal amplification by reversible exchange) hyperpolarization setup with a portable, point-of-care 64 mT clinical MRI scanner are reported. Results from 1H SABRE MRI of pyrazine and nicotinamide are compared with those of benchtop NMR spectroscopy. Comparison with MRI signals from samples with known H2O/D2O ratios allowed quantification of the SABRE enhancements of imaged samples with various substrate concentrations (down to 3 mM). Respective limits of detection and quantification of 3.3 and 10.1 mM were determined with pyrazine 1H polarization (PH) enhancements of ∼1900 (PH ∼0.04%), supporting ongoing and envisioned efforts to realize SABRE-enabled MRI-based molecular imaging.
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Affiliation(s)
- Nadiya Iqbal
- School of Chemical and Biomolecular Sciences, Southern Illinois University, Carbondale, Illinois 62901, United States
| | - Drew O Brittin
- School of Chemical and Biomolecular Sciences, Southern Illinois University, Carbondale, Illinois 62901, United States
| | | | - Md Shahabuddin Alam
- School of Chemical and Biomolecular Sciences, Southern Illinois University, Carbondale, Illinois 62901, United States
| | - Ishani M Senanayake
- School of Chemical and Biomolecular Sciences, Southern Illinois University, Carbondale, Illinois 62901, United States
| | - A Tobi Gafar
- School of Chemical and Biomolecular Sciences, Southern Illinois University, Carbondale, Illinois 62901, United States
| | - Zahid Siraj
- School of Chemical and Biomolecular Sciences, Southern Illinois University, Carbondale, Illinois 62901, United States
| | - Anthony Petrilla
- School of Chemical and Biomolecular Sciences, Southern Illinois University, Carbondale, Illinois 62901, United States
| | - Margaret Pugh
- School of Chemical and Biomolecular Sciences, Southern Illinois University, Carbondale, Illinois 62901, United States
| | - Brockton Tonazzi
- School of Medicine, Southern Illinois University, Carbondale, Illinois 62901, United States
| | | | | | - Laura Sacolick
- Hyperfine Inc., Guilford, Connecticut 06437, United States
| | - Thomas Theis
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Matthew S Rosen
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02129, United States
| | - Eduard Y Chekmenev
- Department of Chemistry, Integrative Biosciences (IBio), Karmanos Cancer Institute (KCI), Wayne State University, Detroit, Michigan 48202, United States
| | - Boyd M Goodson
- School of Chemical and Biomolecular Sciences, Southern Illinois University, Carbondale, Illinois 62901, United States
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14
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Chen Q, Zhang S, Liu W, Sun X, Luo Y, Sun X. Application of emerging technologies in ischemic stroke: from clinical study to basic research. Front Neurol 2024; 15:1400469. [PMID: 38915803 PMCID: PMC11194379 DOI: 10.3389/fneur.2024.1400469] [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: 03/13/2024] [Accepted: 05/24/2024] [Indexed: 06/26/2024] Open
Abstract
Stroke is a primary cause of noncommunicable disease-related death and disability worldwide. The most common form, ischemic stroke, is increasing in incidence resulting in a significant burden on patients and society. Urgent action is thus needed to address preventable risk factors and improve treatment methods. This review examines emerging technologies used in the management of ischemic stroke, including neuroimaging, regenerative medicine, biology, and nanomedicine, highlighting their benefits, clinical applications, and limitations. Additionally, we suggest strategies for technological development for the prevention, diagnosis, and treatment of ischemic stroke.
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Affiliation(s)
- Qiuyan Chen
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing, China
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing, China
| | - Shuxia Zhang
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing, China
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing, China
| | - Wenxiu Liu
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing, China
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing, China
| | - Xiao Sun
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing, China
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing, China
| | - Yun Luo
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing, China
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing, China
| | - Xiaobo Sun
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing, China
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing, China
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15
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Sarraj A, Pujara DK, Campbell BC. Current State of Evidence for Neuroimaging Paradigms in Management of Acute Ischemic Stroke. Ann Neurol 2024; 95:1017-1034. [PMID: 38606939 DOI: 10.1002/ana.26925] [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: 08/29/2023] [Revised: 03/08/2024] [Accepted: 03/11/2024] [Indexed: 04/13/2024]
Abstract
Stroke is the chief differential diagnosis in patient presenting to the emergency room with abrupt onset focal neurological deficits. Neuroimaging, including non-contrast computed tomography (CT), magnetic resonance imaging (MRI), vascular and perfusion imaging, is a cornerstone in the diagnosis and treatment decision-making. This review examines the current state of evidence behind the different imaging paradigms for acute ischemic stroke diagnosis and treatment, including current recommendations from the guidelines. Non-contrast CT brain, or in some centers MRI, can help differentiate ischemic stroke and intracerebral hemorrhage (ICH), a pivotal juncture in stroke diagnosis and treatment algorithm, especially for early window thrombolytics. Advanced imaging such as MRI or perfusion imaging can also assist making a diagnosis of ischemic stroke versus mimics such as migraine, Todd's paresis, or functional disorders. Identification of medium-large vessel occlusions with CT or MR angiography triggers consideration of endovascular thrombectomy (EVT), with additional perfusion imaging help identify salvageable brain tissue in patients who are likely to benefit from reperfusion therapies, particularly in the ≥6 h window. We also review recent advances in neuroimaging and ongoing trials in key therapeutic areas and their imaging selection criteria to inform the readers on potential future transitions into use of neuroimaging for stroke diagnosis and treatment decision making. ANN NEUROL 2024;95:1017-1034.
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Affiliation(s)
- Amrou Sarraj
- University Hospital Cleveland Medical Center-Case Western Reserve University, Neurology, Cleveland, Ohio, USA
| | - Deep K Pujara
- University Hospital Cleveland Medical Center-Case Western Reserve University, Neurology, Cleveland, Ohio, USA
| | - Bruce Cv Campbell
- The Royal Melbourne Hospital-The Florey Institute for Neuroscience and Mental Health, Medicine and Neurology, Parkville, Australia
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16
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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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17
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Anazodo UC, Plessis SD. Imaging without barriers. Science 2024; 384:623-624. [PMID: 38723100 DOI: 10.1126/science.adp0670] [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
Low-field magnetic resonance imaging can be engineered for widespread point-of-care diagnostics.
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Affiliation(s)
- Udunna C Anazodo
- McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery, Montreal Neurological Insitute, McGill University, Montreal, QC, Canada
- Medical Artificial Intelligence Laboratory, Crestview Radiology Ltd., Lagos, Nigeria
| | - Stefan du Plessis
- Department of Psychiatry, Stellenbosch University, Cape Town, South Africa
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18
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Nieboer KH. Rethinking the shoe: is CT perfusion the optimal screening tool for acute stroke patients? Eur Radiol 2024; 34:3059-3060. [PMID: 37851121 PMCID: PMC11126433 DOI: 10.1007/s00330-023-10336-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 08/10/2023] [Accepted: 08/18/2023] [Indexed: 10/19/2023]
Affiliation(s)
- Koenraad H Nieboer
- Department of Radiology and Medical Imaging, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090, Brussels, Belgium.
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19
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Ham AS, Hacker CT, Guo J, Sorby-Adams A, Kimberly WT, Mateen FJ. Feasibility and tolerability of portable, low-field brain MRI for patients with multiple sclerosis. Mult Scler Relat Disord 2024; 85:105515. [PMID: 38489947 DOI: 10.1016/j.msard.2024.105515] [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] [Received: 11/10/2023] [Revised: 01/22/2024] [Accepted: 02/24/2024] [Indexed: 03/17/2024]
Abstract
Low-field, portable MRI (LF-MRI) promises to expand neuroimaging access for patients with multiple sclerosis (MS). We aimed to measure the feasibility and tolerability of LF-MRI for clinical use in 50 people with MS (mean age 46.5 ± 15.3 years; 72 % female; median disease duration 5.9 years), 38 % of whom reported barriers to undergoing MRI, and 34 % of whom were low-income or unemployed. Experience ratings of LF-MRI were strongly positive (mean rating of 9.2 on a ten-point scale). Seventy percent of participants were willing to receive future LF-MRI scans, and 46 % preferred LF-MRI to standard MRI (35 % undecided). The overall feasibility and tolerability of LF-MRI support its integration into a one-stop, patient-centered model of outpatient MS care.
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Affiliation(s)
- Andrew Siyoon Ham
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Jennifer Guo
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Annabel Sorby-Adams
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - W Taylor Kimberly
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Farrah J Mateen
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
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20
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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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21
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Xuan L, Zhang Y, Wu J, He Y, Xu Z. Quantitative brain mapping using magnetic resonance fingerprinting on a 50-mT portable MRI scanner. NMR IN BIOMEDICINE 2024; 37:e5077. [PMID: 38057971 DOI: 10.1002/nbm.5077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 10/17/2023] [Accepted: 11/02/2023] [Indexed: 12/08/2023]
Abstract
Ultralow-field magnetic resonance imaging (ULF-MRI) has broad application prospects because of its portable hardware system and low cost. However, the low B0 magnitude of ULF-MRI results in a reduced signal-to-noise ratio in qualitative images compared with that of commercial high-field MRI, which can affect the visibility and delineation of tissues and lesions. In this work, a magnetic resonance fingerprinting (MRF) approach is applied to a homemade 50-mT ULF-MRI scanner to achieve efficient quantitative brain imaging, which is an original and promising disease-diagnosis approach for portable MRI systems. An inversion recovery fast imaging with steady-state precession-based sequence is utilized for MRF through Cartesian acquisition. A microdictionary analysis method is proposed to select the optimal repetition time and flip angle variation schedule and ensure the best possible tissue discriminative ability of MRF. The T1 and T2 relaxation properties and the B1 + distribution are considered for estimation, and the results are compared with those of gold standard (GS) quantitative imaging or qualitative imaging methods. The phantom experiment indicates that the quantitative values obtained by schedule-optimized MRF show good agreement, and the bias from the GS results is acceptable. The in vivo experiment shows that the relaxation times of white and gray matter estimated by MRF are slightly lower than the reference data, and the relaxation times of lipid are within the range of the reference data. Compared with qualitative MRI under ULF, MRF can intuitively reflect various items of brain tissue information in a single scan, so it is a valuable addition to point-of-care imaging approaches.
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Affiliation(s)
- Liang Xuan
- School of Electrical Engineering, Chongqing University, Chongqing, China
| | - Yuxiang Zhang
- School of Electrical Engineering, Chongqing University, Chongqing, China
| | - Jiamin Wu
- Shenzhen Academy of Aerospace Technology, Shenzhen, China
| | - Yucheng He
- Shenzhen Academy of Aerospace Technology, Shenzhen, China
| | - Zheng Xu
- School of Electrical Engineering, Chongqing University, Chongqing, China
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22
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Meng F, Guo Y, Wei H, Xu Z. Development of a Helmet-Shape Dual-Channel RF coil for brain imaging at 54 mT using inverse boundary element method. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2024; 360:107636. [PMID: 38377783 DOI: 10.1016/j.jmr.2024.107636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 02/04/2024] [Accepted: 02/08/2024] [Indexed: 02/22/2024]
Abstract
Very-low field (VLF) magnetic resonance imaging (MRI) offers advantages in term of size, weight, cost, and the absence of robust shielding requirements. However, it encounters challenges in maintaining a high signal-to-noise ratio (SNR) due to low magnetic fields (below 100 mT). Developing a close-fitting radio frequency (RF) receive coil is crucial to improve the SNR. In this study, we devised and optimized a helmet-shaped dual-channel RF receive coil tailored for brain imaging at a magnetic field strength of 54 mT (2.32 MHz). The methodology integrates the inverse boundary element method (IBEM) to formulate initial coil structures and wiring patterns, followed by optimization through introducing regularization terms. This approach frames the design process as an inverse problem, ensuring a close fit to the head contour. Combining theoretical optimization with physical measurements of the coil's AC resistance, we identified the optimal loop count for both axial and radial coils as nine and eight loops, respectively. The effectiveness of the designed dual-channel coil was verified through the imaging of a CuSO4 phantom and a healthy volunteer's brain. Notably, the in-vivo images exhibited an approximate 16-25 % increase in SNR with poorer B1 homogeneity compared to those obtained using single-channel coils. The high-quality images achieved by T1, T2-weighted, and fluid-attenuated inversion-recovery (FLAIR) protocols enhance the diagnostic potential of VLF MRI, particularly in cases of cerebral stroke and trauma patients. This study underscores the adaptability of the design methodology for the customization of RF coil structures in alignment with individual imaging requirements.
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Affiliation(s)
- Fanqin Meng
- School of Electrical Engineering, Chongqing University, Chongqing 400044, China
| | - Yi Guo
- Central Hospital, Chongqing University, Chongqing 400014, China
| | - He Wei
- School of Electrical Engineering, Chongqing University, Chongqing 400044, China
| | - Zheng Xu
- School of Electrical Engineering, Chongqing University, Chongqing 400044, China.
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23
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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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24
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Tarui T, Gimovsky AC, Madan N. Fetal neuroimaging applications for diagnosis and counseling of brain anomalies: Current practice and future diagnostic strategies. Semin Fetal Neonatal Med 2024; 29:101525. [PMID: 38632010 PMCID: PMC11156536 DOI: 10.1016/j.siny.2024.101525] [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] [Indexed: 04/19/2024]
Abstract
Advances in fetal brain neuroimaging, especially fetal neurosonography and brain magnetic resonance imaging (MRI), allow safe and accurate anatomical assessments of fetal brain structures that serve as a foundation for prenatal diagnosis and counseling regarding fetal brain anomalies. Fetal neurosonography strategically assesses fetal brain anomalies suspected by screening ultrasound. Fetal brain MRI has unique technological features that overcome the anatomical limits of smaller fetal brain size and the unpredictable variable of intrauterine motion artifact. Recent studies of fetal brain MRI provide evidence of improved diagnostic and prognostic accuracy, beginning with prenatal diagnosis. Despite technological advances over the last several decades, the combined use of different qualitative structural biomarkers has limitations in providing an accurate prognosis. Quantitative analyses of fetal brain MRIs offer measurable imaging biomarkers that will more accurately associate with clinical outcomes. First-trimester ultrasound opens new opportunities for risk assessment and fetal brain anomaly diagnosis at the earliest time in pregnancy. This review includes a case vignette to illustrate how fetal brain MRI results interpreted by the fetal neurologist can improve diagnostic perspectives. The strength and limitations of conventional ultrasound and fetal brain MRI will be compared with recent research advances in quantitative methods to better correlate fetal neuroimaging biomarkers of neuropathology to predict functional childhood deficits. Discussion of these fetal sonogram and brain MRI advances will highlight the need for further interdisciplinary collaboration using complementary skills to continue improving clinical decision-making following precision medicine principles.
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Affiliation(s)
- Tomo Tarui
- Pediatric Neurology, Pediatrics, Hasbro Children's Hospital, The Warren Alpert Medical School of Brown University, Providence, RI, USA.
| | - Alexis C Gimovsky
- Maternal Fetal Medicine, Obstetrics and Gynecology, Women & Infants Hospital of Rhode Island, The Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Neel Madan
- Neuroradiology, Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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25
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Foschi M, Galante A, Ornello R, Necozione S, Marini C, Muselli M, Achard PO, Fratocchi L, Vinci SL, Cavallaro M, Silvestrini M, Polonara G, Marcheselli S, Straffi L, Colasurdo M, Sorrentino L, Franconi E, Alecci M, Caulo M, Sacco S. Point-Of-Care low-field MRI in acute Stroke (POCS): protocol for a multicentric prospective open-label study evaluating diagnostic accuracy. BMJ Open 2024; 14:e075614. [PMID: 38296269 PMCID: PMC10831427 DOI: 10.1136/bmjopen-2023-075614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 01/16/2024] [Indexed: 02/03/2024] Open
Abstract
INTRODUCTION Fast and accurate diagnosis of acute stroke is crucial to timely initiate reperfusion therapies. Conventional high-field (HF) MRI yields the highest accuracy in discriminating early ischaemia from haemorrhages and mimics. Rapid access to HF-MRI is often limited by contraindications or unavailability. Low-field (LF) MRI (<0.5T) can detect several types of brain injury, including ischaemic and haemorrhagic stroke. Implementing LF-MRI in acute stroke care may offer several advantages, including extended applicability, increased safety, faster administration, reduced staffing and costs. This multicentric prospective open-label trial aims to evaluate the diagnostic accuracy of LF-MRI, as a tool to guide treatment decision in acute stroke. METHODS AND ANALYSIS Consecutive patients accessing the emergency department with suspected stroke dispatch will be recruited at three Italian study units: Azienda Sanitaria Locale (ASL) Abruzzo 1 and 2, Istituto di Ricerca e Cura a Carattere Scientifico (IRCCS) Humanitas Research Hospital. The estimated sample size is 300 patients. Anonymised clinical and LF-MRI data, along with conventional neuroimaging data, will be independently assessed by two external units: Marche Polytechnic University and 'G. Martino' Polyclinic University Hospital. Both units will independently adjudicate the best treatment option, while the latter will provide historical HF-MRI data to develop artificial intelligence algorithms for LF-MRI images interpretation (Free University of Bozen-Bolzano). Agreement with conventional neuroimaging will be evaluated at different time points: hyperacute, acute (24 hours), subacute (72 hours), at discharge and chronic (4 weeks). Further investigations will include feasibility study to develop a mobile stroke unit equipped with LF-MRI and cost-effectiveness analysis. This trial will provide necessary data to validate the use of LF-MRI in acute stroke care. ETHICS AND DISSEMINATION The study was approved by the Research Ethics Committee of the Abruzzo Region (CEtRA) on 11 May 2023 (approval code: richyvgrg). Results will be disseminated in peer-reviewed journals and presented in academic conferences. TRIAL REGISTRATION NUMBER NCT05816213; Pre-Results.
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Affiliation(s)
- Matteo Foschi
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Angelo Galante
- Department of Life, Health & Environmental Sciences, University of L'Aquila, L'Aquila, Italy
- National Institute for Nuclear Physics, Gran Sasso National Laboratory, L'Aquila, Italy
- SPIN-CNR, c/o Department of Physical and Chemical Science, University of L'Aquila, L'Aquila, Italy
| | - Raffaele Ornello
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Stefano Necozione
- Department of Life, Health & Environmental Sciences, University of L'Aquila, L'Aquila, Italy
| | - Carmine Marini
- Department of Life, Health & Environmental Sciences, University of L'Aquila, L'Aquila, Italy
| | - Mario Muselli
- Department of Life, Health & Environmental Sciences, University of L'Aquila, L'Aquila, Italy
| | - Paola Olimpia Achard
- Department of Industrial and Information Engineering and Economics, University of L'Aquila, L'Aquila, Italy
| | - Luciano Fratocchi
- Department of Industrial and Information Engineering and Economics, University of L'Aquila, L'Aquila, Italy
| | - Sergio Lucio Vinci
- Department of Biomorf, University of Messina, UOC Neuroradiology, Messina, Italy
| | - Marco Cavallaro
- Department of Biomorf, University of Messina, UOC Neuroradiology, Messina, Italy
| | - Mauro Silvestrini
- Department of Experimental and Clinical Medicine, Neurological Clinic, Marche Polytechnic University, Ancona, Italy
| | - Gabriele Polonara
- Department of Odontostomatological and Specialized Clinical Sciences, Polytechnic University of Marche, Ancona, Italy
| | - Simona Marcheselli
- Emergency Neurology and Stroke Unit, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Laura Straffi
- Emergency Neurology and Stroke Unit, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Marco Colasurdo
- Department of Neuroscience and Clinical Sciences, University of Chieti, Chieti, Italy
| | - Luca Sorrentino
- Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, Cassino, Italy
| | - Enrico Franconi
- Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano, Italy
| | - Marcello Alecci
- Department of Life, Health & Environmental Sciences, University of L'Aquila, L'Aquila, Italy
- National Institute for Nuclear Physics, Gran Sasso National Laboratory, L'Aquila, Italy
- SPIN-CNR, c/o Department of Physical and Chemical Science, University of L'Aquila, L'Aquila, Italy
| | - Massimo Caulo
- Department of Neuroscience and Clinical Sciences, University of Chieti, Chieti, Italy
| | - Simona Sacco
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
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26
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Cho SM, Khanduja S, Wilcox C, Dinh K, Kim J, Kang JK, Chinedozi ID, Darby Z, Acton M, Rando H, Briscoe J, Bush E, Sair HI, Pitts J, Arlinghaus LR, Wandji ACN, Moreno E, Torres G, Akkanti B, Gavito-Higuera J, Keller S, Choi HA, Kim BS, Gusdon A, Whitman GJ. Clinical Use of Bedside Portable Low-field Brain Magnetic Resonance Imaging in Patients on ECMO: The Results from Multicenter SAFE MRI ECMO Study. RESEARCH SQUARE 2024:rs.3.rs-3858221. [PMID: 38313271 PMCID: PMC10836091 DOI: 10.21203/rs.3.rs-3858221/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
Purpose Early detection of acute brain injury (ABI) is critical for improving survival for patients with extracorporeal membrane oxygenation (ECMO) support. We aimed to evaluate the safety of ultra-low-field portable MRI (ULF-pMRI) and the frequency and types of ABI observed during ECMO support. Methods We conducted a multicenter prospective observational study (NCT05469139) at two academic tertiary centers (August 2022-November 2023). Primary outcomes were safety and validation of ULF-pMRI in ECMO, defined as exam completion without adverse events (AEs); secondary outcomes were ABI frequency and type. Results ULF-pMRI was performed in 50 patients with 34 (68%) on venoarterial (VA)-ECMO (11 central; 23 peripheral) and 16 (32%) with venovenous (VV)-ECMO (9 single lumen; 7 double lumen). All patients were imaged successfully with ULF-pMRI, demonstrating discernible intracranial pathologies with good quality. AEs occurred in 3 (6%) patients (2 minor; 1 serious) without causing significant clinical issues.ABI was observed in ULF-pMRI scans for 22 patients (44%): ischemic stroke (36%), intracranial hemorrhage (6%), and hypoxic-ischemic brain injury (4%). Of 18 patients with both ULF-pMRI and head CT (HCT) within 24 hours, ABI was observed in 9 patients with 10 events: 8 ischemic (8 observed on ULF-oMRI, 4 on HCT) and 2 hemorrhagic (1 observed on ULF-pMRI, 2 on HCT). Conclusions ULF-pMRI was shown to be safe and valid in ECMO patients across different ECMO cannulation strategies. The incidence of ABI was high, and ULF-pMRI may more sensitive to ischemic ABI than HCT. ULF-pMRI may benefit both clinical care and future studies of ECMO-associated ABI.
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Affiliation(s)
| | | | | | - Kha Dinh
- UTHSC: The University of Texas Health Science Center at Houston
| | - Jiah Kim
- Johns Hopkins Hospital: Johns Hopkins Medicine
| | | | | | | | | | | | | | - Errol Bush
- Johns Hopkins Hospital: Johns Hopkins Medicine
| | | | | | | | | | - Elena Moreno
- UTHSC: The University of Texas Health Science Center at Houston
| | - Glenda Torres
- UTHSC: The University of Texas Health Science Center at Houston
| | - Bindu Akkanti
- UTHSC: The University of Texas Health Science Center at Houston
| | | | | | - HuiMahn A Choi
- UTHSC: The University of Texas Health Science Center at Houston
| | - Bo Soo Kim
- Johns Hopkins Hospital: Johns Hopkins Medicine
| | - Aaron Gusdon
- UTHSC: The University of Texas Health Science Center at Houston
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27
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Sherman SE, Zammit AS, Heo WS, Rosen MS, Cima MJ. Single-sided magnetic resonance-based sensor for point-of-care evaluation of muscle. Nat Commun 2024; 15:440. [PMID: 38199994 PMCID: PMC10782019 DOI: 10.1038/s41467-023-44561-9] [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: 09/12/2023] [Accepted: 12/19/2023] [Indexed: 01/12/2024] Open
Abstract
Magnetic resonance imaging is a widespread clinical tool for the detection of soft tissue morphology and pathology. However, the clinical deployment of magnetic resonance imaging scanners is ultimately limited by size, cost, and space constraints. Here, we discuss the design and performance of a low-field single-sided magnetic resonance sensor intended for point-of-care evaluation of skeletal muscle in vivo. The 11 kg sensor has a penetration depth of >8 mm, which allows for an accurate analysis of muscle tissue and can avoid signal from more proximal layers, including subcutaneous adipose tissue. Low operational power and shielding requirements are achieved through the design of a permanent magnet array and surface transceiver coil. The sensor can acquire high signal-to-noise measurements in minutes, making it practical as a point-of-care tool for many quantitative diagnostic measurements, including T2 relaxometry. In this work, we present the in vitro and human in vivo performance of the device for muscle tissue evaluation.
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Affiliation(s)
- Sydney E Sherman
- Harvard-MIT Program in Health Science and Technology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Alexa S Zammit
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Won-Seok Heo
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Matthew S Rosen
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, 02129, USA
- Harvard Medical School, Boston, MA, 02115, USA
- Department of Physics, Harvard University, Cambridge, MA, 02138, USA
| | - Michael J Cima
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
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28
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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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29
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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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Samaniego EA, Boltze J, Lyden PD, Hill MD, Campbell BCV, Silva GS, Sheth KN, Fisher M, Hillis AE, Nguyen TN, Carone D, Favilla CG, Deljkich E, Albers GW, Heit JJ, Lansberg MG. Priorities for Advancements in Neuroimaging in the Diagnostic Workup of Acute Stroke. Stroke 2023; 54:3190-3201. [PMID: 37942645 PMCID: PMC10841844 DOI: 10.1161/strokeaha.123.044985] [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] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Accepted: 10/03/2023] [Indexed: 11/10/2023]
Abstract
STAIR XII (12th Stroke Treatment Academy Industry Roundtable) included a workshop to discuss the priorities for advancements in neuroimaging in the diagnostic workup of acute ischemic stroke. The workshop brought together representatives from academia, industry, and government. The participants identified 10 critical areas of priority for the advancement of acute stroke imaging. These include enhancing imaging capabilities at primary and comprehensive stroke centers, refining the analysis and characterization of clots, establishing imaging criteria that can predict the response to reperfusion, optimizing the Thrombolysis in Cerebral Infarction scale, predicting first-pass reperfusion outcomes, improving imaging techniques post-reperfusion therapy, detecting early ischemia on noncontrast computed tomography, enhancing cone beam computed tomography, advancing mobile stroke units, and leveraging high-resolution vessel wall imaging to gain deeper insights into pathology. Imaging in acute ischemic stroke treatment has advanced significantly, but important challenges remain that need to be addressed. A combined effort from academic investigators, industry, and regulators is needed to improve imaging technologies and, ultimately, patient outcomes.
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Affiliation(s)
- Edgar A. Samaniego
- Department of Neurology, Radiology and Neurosurgery, University of Iowa Carver College of Medicine, Iowa City, Iowa, United States
| | - Johannes Boltze
- School of Life Sciences, The University of Warwick, Coventry, United Kingdom
| | - Patrick D. Lyden
- Zilkha Neurogenetic Institute of the Keck School of Medicine at USC, Los Angeles, California, United States
| | - Michael D. Hill
- Department of Clinical Neuroscience & Hotchkiss Brain Institute, University of Calgary & Foothills Medical Centre, Calgary, Canada
| | - Bruce CV Campbell
- Department of Medicine and Neurology, Royal Melbourne Hospital, University of Melbourne, Parkville, Victoria, Australia
| | - Gisele Sampaio Silva
- Department of Neurology and Neurosurgery, Federal University of São Paulo, São Paulo, Brazil
| | - Kevin N Sheth
- Department of Neurology, Division of Neurocritical Care and Emergency Neurology, Yale School of Medicine, New Haven, United States
| | - Marc Fisher
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States
| | - Argye E. Hillis
- Department of Neurology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, United Stated
| | - Thanh N. Nguyen
- Department of Neurology, Boston Medical Center, Massachusetts, United States
| | - Davide Carone
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Christopher G. Favilla
- Department of Neurology, University of Pennsylvania Philadelphia, Pennsylvania, Unites States
| | | | - Gregory W. Albers
- Department of Neurology, Stanford University, Stanford, California, United States
| | - Jeremy J. Heit
- Department of Radiology and Neurosurgery, Stanford University, Stanford, California, United States
| | - Maarten G Lansberg
- Department of Neurology, Stanford University, Stanford, California, United States
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31
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Johnson PM, Lui YW. The deep route to low-field MRI with high potential. Nature 2023; 623:700-701. [PMID: 37964114 DOI: 10.1038/d41586-023-03531-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
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32
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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] [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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Guo X, Dye J. Modern Prehospital Screening Technology for Emergent Neurovascular Disorders. Adv Biol (Weinh) 2023; 7:e2300174. [PMID: 37357150 DOI: 10.1002/adbi.202300174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 05/14/2023] [Indexed: 06/27/2023]
Abstract
Stroke is a serious neurological disease and a significant contributor to disability worldwide. Traditional in-hospital imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI) remain the standard modalities for diagnosing stroke. The development of prehospital stroke detection devices may facilitate earlier diagnosis, initiation of stroke care, and ultimately better patient outcomes. In this review, the authors summarize the features of eight stroke detection devices using noninvasive brain scanning technology. The review summarizes the features of stroke detection devices including portable CT, MRI, transcranial Doppler ultrasound , microwave tomographic imaging, electroencephalography, near-infrared spectroscopy, volumetric impedance phaseshift spectroscopy, and cranial accelerometry. The technologies utilized, the indications for application, the environments indicated for application, the physical features of the eight stroke detection devices, and current commercial products are discussed. As technology advances, multiple portable stroke detection instruments exhibit the promising potential to expedite the diagnosis of stroke and enhance the time taken for treatment, ultimately aiding in prehospital stroke triage.
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Affiliation(s)
- Xiaofan Guo
- Department of Neurology, Loma Linda University, Loma Linda, CA, 92354, USA
| | - Justin Dye
- Department of Neurosurgery, Loma Linda University, Loma Linda, CA, 92354, USA
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34
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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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35
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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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36
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Man C, Lau V, Su S, Zhao Y, Xiao L, Ding Y, Leung GK, Leong AT, Wu EX. Deep learning enabled fast 3D brain MRI at 0.055 tesla. SCIENCE ADVANCES 2023; 9:eadi9327. [PMID: 37738341 PMCID: PMC10516503 DOI: 10.1126/sciadv.adi9327] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 08/21/2023] [Indexed: 09/24/2023]
Abstract
In recent years, there has been an intensive development of portable ultralow-field magnetic resonance imaging (MRI) for low-cost, shielding-free, and point-of-care applications. However, its quality is poor and scan time is long. We propose a fast acquisition and deep learning reconstruction framework to accelerate brain MRI at 0.055 tesla. The acquisition consists of a single average three-dimensional (3D) encoding with 2D partial Fourier sampling, reducing the scan time of T1- and T2-weighted imaging protocols to 2.5 and 3.2 minutes, respectively. The 3D deep learning leverages the homogeneous brain anatomy available in high-field human brain data to enhance image quality, reduce artifacts and noise, and improve spatial resolution to synthetic 1.5-mm isotropic resolution. Our method successfully overcomes low-signal barrier, reconstructing fine anatomical structures that are reproducible within subjects and consistent across two protocols. It enables fast and quality whole-brain MRI at 0.055 tesla, with potential for widespread biomedical applications.
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Affiliation(s)
- Christopher Man
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
| | - Vick Lau
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
| | - Shi Su
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
| | - Yujiao Zhao
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
| | - Linfang Xiao
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
| | - Ye Ding
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
| | - Gilberto K. K. Leung
- Department of Surgery, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
| | - Alex T. L. Leong
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
| | - Ed X. Wu
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
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Cima M, Sherman S, Zammit A, Heo WS, Rosen M. Single-sided magnetic resonance-based sensor for point-of-care evaluation of muscle. RESEARCH SQUARE 2023:rs.3.rs-3335248. [PMID: 37790511 PMCID: PMC10543496 DOI: 10.21203/rs.3.rs-3335248/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Magnetic resonance (MR) imaging is a powerful clinical tool for the detection of soft tissue morphology and pathology, which often provides actionable diagnostic information to clinicians. Its clinical use is largely limited due to size, cost, time, and space constraints. Here, we discuss the design and performance of a low-field single-sided MR sensor intended for point-of-care (POC) evaluation of skeletal muscle in vivo. The 11kg sensor has a penetration depth of > 8 mm, which allows for an accurate analysis of muscle tissue and can avoid signal from more proximal layers, including subcutaneous adipose tissue. Low operational power and minimal shielding requirements are achieved through the design of a permanent magnet array and surface transceiver coil. We present the in vitro and human in vivo performance of the device for muscle tissue evaluation. The sensor can acquire high signal-to-noise (SNR > 150) measurements in minutes, making it practical as a POC tool for many quantitative diagnostic measurements, including T2 relaxometry.
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Affiliation(s)
| | | | | | | | - Matthew Rosen
- Massachusetts General Hospital and Harvard Medical School
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38
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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: 10] [Impact Index Per Article: 10.0] [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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He Q, Li W, Shi Y, Yu Y, Geng W, Sun Z, Wang RK. SpeCamX: mobile app that turns unmodified smartphones into multispectral imagers. BIOMEDICAL OPTICS EXPRESS 2023; 14:4929-4946. [PMID: 37791269 PMCID: PMC10545193 DOI: 10.1364/boe.497602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 08/13/2023] [Accepted: 08/14/2023] [Indexed: 10/05/2023]
Abstract
We present the development of SpeCamX, a mobile application that enables an unmodified smartphone into a multispectral imager. Multispectral imaging provides detailed spectral information about objects or scenes, but its accessibility has been limited due to its specialized requirements for the device. SpeCamX overcomes this limitation by utilizing the RGB photographs captured by smartphones and converting them into multispectral images spanning a range of 420 to 680 nm without a need for internal modifications or external attachments. The app also includes plugin functions for extracting medical information from the resulting multispectral data cube. In a clinical study, SpeCamX was used to implement an augmented smartphone bilirubinometer, predicting blood bilirubin levels (BBL) with superior performance in accuracy, efficiency and stability compared to default smartphone cameras. This innovative technology democratizes multispectral imaging, making it accessible to a wider audience and opening new possibilities for both medical and non-medical applications.
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Affiliation(s)
- Qinghua He
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Science, Changchun, Jilin 130033, China
- Department of Bioengineering, University of Washington, Seattle, Washington 98105, USA
| | - Wanyu Li
- Department of Hepatobiliary and pancreatic Medicine, The first Hospital of Jilin University NO.71 Xinmin Street, Changchun, Jilin 130021, China
| | - Yaping Shi
- Department of Bioengineering, University of Washington, Seattle, Washington 98105, USA
| | - Yi Yu
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Science, Changchun, Jilin 130033, China
| | - Wenqian Geng
- Department of Hepatobiliary and pancreatic Medicine, The first Hospital of Jilin University NO.71 Xinmin Street, Changchun, Jilin 130021, China
| | - Zhiyuan Sun
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Science, Changchun, Jilin 130033, China
| | - Ruikang K Wang
- Department of Bioengineering, University of Washington, Seattle, Washington 98105, USA
- Department of Ophthalmology, University of Washington, Seattle, Washington 98109, USA
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40
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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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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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42
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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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Lyu M, Mei L, Huang S, Liu S, Li Y, Yang K, Liu Y, Dong Y, Dong L, Wu EX. M4Raw: A multi-contrast, multi-repetition, multi-channel MRI k-space dataset for low-field MRI research. Sci Data 2023; 10:264. [PMID: 37164976 PMCID: PMC10172399 DOI: 10.1038/s41597-023-02181-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 04/25/2023] [Indexed: 05/12/2023] Open
Abstract
Recently, low-field magnetic resonance imaging (MRI) has gained renewed interest to promote MRI accessibility and affordability worldwide. The presented M4Raw dataset aims to facilitate methodology development and reproducible research in this field. The dataset comprises multi-channel brain k-space data collected from 183 healthy volunteers using a 0.3 Tesla whole-body MRI system, and includes T1-weighted, T2-weighted, and fluid attenuated inversion recovery (FLAIR) images with in-plane resolution of ~1.2 mm and through-plane resolution of 5 mm. Importantly, each contrast contains multiple repetitions, which can be used individually or to form multi-repetition averaged images. After excluding motion-corrupted data, the partitioned training and validation subsets contain 1024 and 240 volumes, respectively. To demonstrate the potential utility of this dataset, we trained deep learning models for image denoising and parallel imaging tasks and compared their performance with traditional reconstruction methods. This M4Raw dataset will be valuable for the development of advanced data-driven methods specifically for low-field MRI. It can also serve as a benchmark dataset for general MRI reconstruction algorithms.
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Affiliation(s)
- Mengye Lyu
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China.
| | - Lifeng Mei
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China
| | - Shoujin Huang
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China
| | - Sixing Liu
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China
| | - Yi Li
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China
| | - Kexin Yang
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China
| | - Yilong Liu
- Guangdong-Hongkong-Macau Institute of CNS Regeneration, Key Laboratory of CNS Regeneration (Ministry of Education), Jinan University, Guangzhou, China
| | - Yu Dong
- Department of Neurosurgery, Shenzhen Samii Medical Center, Shenzhen, China
| | - Linzheng Dong
- Department of Neurosurgery, Shenzhen Samii Medical Center, Shenzhen, China
| | - Ed X Wu
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
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Richards CT, Oostema JA, Chapman SN, Mamer LE, Brandler ES, Alexandrov AW, Czap AL, Martinez-Gutierrez JC, Martin-Gill C, Panchal AR, McMullan JT, Zachrison KS. Prehospital Stroke Care Part 2: On-Scene Evaluation and Management by Emergency Medical Services Practitioners. Stroke 2023; 54:1416-1425. [PMID: 36866672 PMCID: PMC10133016 DOI: 10.1161/strokeaha.123.039792] [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: 01/03/2023] [Accepted: 02/02/2023] [Indexed: 03/04/2023]
Abstract
The prehospital phase is a critical component of delivering high-quality acute stroke care. This topical review discusses the current state of prehospital acute stroke screening and transport, as well as new and emerging advances in prehospital diagnosis and treatment of acute stroke. Topics include prehospital stroke screening, stroke severity screening, emerging technologies to aid in the identification and diagnosis of acute stroke in the prehospital setting, prenotification of receiving emergency departments, decision support for destination determination, and the capabilities and opportunities for prehospital stroke treatment in mobile stroke units. Further evidence-based guideline development and implementation of new technologies are critical for ongoing improvements in prehospital stroke care.
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Affiliation(s)
- Christopher T. Richards
- Department of Emergency Medicine, University of Cincinnati College of Medicine, Cincinnati, OH
| | - J. Adam Oostema
- Department of Emergency Medicine, Michigan State University College of Human Medicine, Grand Rapids, MI
| | | | - Lauren E. Mamer
- Department of Emergency Medicine, University of Michigan, Ann Arbor, MI
| | - Ethan S. Brandler
- Department of Emergency Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY
| | - Anne W. Alexandrov
- College of Nursing, University of Tennessee Health Science Center, Memphis, TN
| | - Alexandra L. Czap
- Department of Neurology, University of Texas Houston McGovern Medical School, Houston, TX
| | | | | | - Ashish R. Panchal
- Department of Emergency Medicine, The Ohio State University Wexner Medical Center, Columbus, OH
| | - Jason T. McMullan
- Department of Emergency Medicine, University of Cincinnati College of Medicine, Cincinnati, OH
| | - Kori S. Zachrison
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA
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Sari A, Saleh Velez FG, Muntz N, Bulwa Z, Prabhakaran S. Validating Existing Scales for Identification of Acute Stroke in an Inpatient Setting. Neurohospitalist 2023; 13:137-143. [PMID: 37064928 PMCID: PMC10091444 DOI: 10.1177/19418744221144343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023] Open
Abstract
Background and Purpose A significant proportion of strokes occur while patients are hospitalized for other reasons. Numerous stroke scales have been developed and validated for use in pre-hospital and emergency department settings, and there is growing interest to adapt these scales for use in the inpatient setting. We aimed to validate existing stroke scales for inpatient stroke codes. Methods We retrospectively reviewed charts from inpatient stroke code activations at an urban academic medical center from January 2016 through December 2018. Receiver operating characteristic analysis was performed for each specified stroke scale including NIHSS, FAST, BE-FAST, 2CAN, FABS, TeleStroke Mimic, and LAMS. We also used logistic regression to identify independent predictors of stroke and to derive a novel scale. Results Of the 958 stroke code activations reviewed, 151 (15.8%) had a final diagnosis of ischemic or hemorrhagic stroke. The area under the curve (AUC) of existing scales varied from .465 (FABS score) to .563 (2CAN score). Four risk factors independently predicted stroke: (1) recent cardiovascular procedure, (2) platelet count less than 50 × 109 per liter, (3) gaze deviation, and (4) presence of unilateral leg weakness. Combining these 4 factors into a new score yielded an AUC of .653 (95% confidence interval [CI] .604-.702). Conclusion This study suggests that currently available stroke scales may not be sufficient to differentiate strokes from mimics in the inpatient setting. Our data suggest that novel approaches may be required to help with diagnosis in this unique population.
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Affiliation(s)
- Adriana Sari
- Pritzker School of Medicine, University of Chicago, Chicago, IL, USA
| | - Faddi G. Saleh Velez
- Department of Neurology, University of Chicago, Chicago, IL, USA
- Department of Neurology, University of Miami, Miami, FL, USA
| | - Nathan Muntz
- Pritzker School of Medicine, University of Chicago, Chicago, IL, USA
| | - Zachary Bulwa
- Department of Neurology, University of Chicago, Chicago, IL, USA
- NorthShore University Health
System, Chicago, IL, USA
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46
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Perron S, Ouriadov A. Hyperpolarized 129Xe MRI at low field: Current status and future directions. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2023; 348:107387. [PMID: 36731353 DOI: 10.1016/j.jmr.2023.107387] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 12/07/2022] [Accepted: 01/23/2023] [Indexed: 06/18/2023]
Abstract
Magnetic Resonance Imaging (MRI) is dictated by the magnetization of the sample, and is thus a low-sensitivity imaging method. Inhalation of hyperpolarized (HP) noble gases, such as helium-3 and xenon-129, is a non-invasive, radiation-risk free imaging technique permitting high resolution imaging of the lungs and pulmonary functions, such as the lung microstructure, diffusion, perfusion, gas exchange, and dynamic ventilation. Instead of increasing the magnetic field strength, the higher spin polarization achievable from this method results in significantly higher net MR signal independent of tissue/water concentration. Moreover, the significantly longer apparent transverse relaxation time T2* of these HP gases at low magnetic field strengths results in fewer necessary radiofrequency (RF) pulses, permitting larger flip angles; this allows for high-sensitivity imaging of in vivo animal and human lungs at conventionally low (<0.5 T) field strengths and suggests that the low field regime is optimal for pulmonary MRI using hyperpolarized gases. In this review, theory on the common spin-exchange optical-pumping method of hyperpolarization and the field dependence of the MR signal of HP gases are presented, in the context of human lung imaging. The current state-of-the-art is explored, with emphasis on both MRI hardware (low field scanners, RF coils, and polarizers) and image acquisition techniques (pulse sequences) advancements. Common challenges surrounding imaging of HP gases and possible solutions are discussed, and the future of low field hyperpolarized gas MRI is posed as being a clinically-accessible and versatile imaging method, circumventing the siting restrictions of conventional high field scanners and bringing point-of-care pulmonary imaging to global facilities.
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Affiliation(s)
- Samuel Perron
- Department of Physics and Astronomy, The University of Western Ontario, London, Ontario, Canada.
| | - Alexei Ouriadov
- Department of Physics and Astronomy, The University of Western Ontario, London, Ontario, Canada; Lawson Health Research Institute, London, Ontario, Canada; School of Biomedical Engineering, Faculty of Engineering, The University of Western Ontario, London, Ontario, Canada
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47
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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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48
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Pinter NK. The Right Imaging Protocol for the Right Patient. Continuum (Minneap Minn) 2023; 29:16-26. [PMID: 36795871 DOI: 10.1212/con.0000000000001209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
OBJECTIVE This article provides a high-level overview of the challenge of choosing the right imaging approach for an individual patient. It also presents a generalizable approach that can be applied to practice regardless of specific imaging technologies. ESSENTIAL POINTS This article constitutes an introduction to the in-depth, topic-focused analyses in the rest of this issue. It examines the broad principles that guide placing a patient on the right diagnostic trajectory, illustrated with real-life examples of current protocol recommendations and cases of advanced imaging techniques, as well as some thought experiments. Thinking about diagnostic imaging strictly in terms of imaging protocols is often inefficient because these protocols can be vague and have numerous variations. Broadly defined protocols may be sufficient, but their successful use often depends largely on the particular circumstances, with special emphasis on the relationship between neurologists and radiologists.
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The potential role of ischaemia-reperfusion injury in chronic, relapsing diseases such as rheumatoid arthritis, Long COVID, and ME/CFS: evidence, mechanisms, and therapeutic implications. Biochem J 2022; 479:1653-1708. [PMID: 36043493 PMCID: PMC9484810 DOI: 10.1042/bcj20220154] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 08/09/2022] [Accepted: 08/10/2022] [Indexed: 02/07/2023]
Abstract
Ischaemia–reperfusion (I–R) injury, initiated via bursts of reactive oxygen species produced during the reoxygenation phase following hypoxia, is well known in a variety of acute circumstances. We argue here that I–R injury also underpins elements of the pathology of a variety of chronic, inflammatory diseases, including rheumatoid arthritis, ME/CFS and, our chief focus and most proximally, Long COVID. Ischaemia may be initiated via fibrin amyloid microclot blockage of capillaries, for instance as exercise is started; reperfusion is a necessary corollary when it finishes. We rehearse the mechanistic evidence for these occurrences here, in terms of their manifestation as oxidative stress, hyperinflammation, mast cell activation, the production of marker metabolites and related activities. Such microclot-based phenomena can explain both the breathlessness/fatigue and the post-exertional malaise that may be observed in these conditions, as well as many other observables. The recognition of these processes implies, mechanistically, that therapeutic benefit is potentially to be had from antioxidants, from anti-inflammatories, from iron chelators, and via suitable, safe fibrinolytics, and/or anti-clotting agents. We review the considerable existing evidence that is consistent with this, and with the biochemical mechanisms involved.
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50
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Basser P. Detection of stroke by portable, low-field MRI: A milestone in medical imaging. SCIENCE ADVANCES 2022; 8:eabp9307. [PMID: 35442726 DOI: 10.1126/sciadv.abp9307] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Portable, low-field magnetic resonance imagers can aid the clinical assessment of stroke. They may also help democratize access to scarce medical imaging resources.
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Affiliation(s)
- Peter Basser
- Section on Quantitative Imaging and Tissue Sciences, NICHD, NIH, Bethesda, MD, USA.
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