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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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2
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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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3
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Gaulton TG, Xin Y, Victor M, Nova A, Cereda M. Imaging the pulmonary vasculature in acute respiratory distress syndrome. Nitric Oxide 2024; 147:6-12. [PMID: 38588918 DOI: 10.1016/j.niox.2024.04.004] [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: 01/30/2024] [Revised: 03/21/2024] [Accepted: 04/03/2024] [Indexed: 04/10/2024]
Abstract
Acute respiratory distress syndrome (ARDS) is characterized by a redistribution of regional lung perfusion that impairs gas exchange. While speculative, experimental evidence suggests that perfusion redistribution may contribute to regional inflammation and modify disease progression. Unfortunately, tools to visualize and quantify lung perfusion in patients with ARDS are lacking. This review explores recent advances in perfusion imaging techniques that aim to understand the pulmonary circulation in ARDS. Dynamic contrast-enhanced computed tomography captures first-pass kinetics of intravenously injected dye during continuous scan acquisitions. Different contrast characteristics and kinetic modeling have improved its topographic measurement of pulmonary perfusion with high spatial and temporal resolution. Dual-energy computed tomography can map the pulmonary blood volume of the whole lung with limited radiation exposure, enabling its application in clinical research. Electrical impedance tomography can obtain serial topographic assessments of perfusion at the bedside in response to treatments such as inhaled nitric oxide and prone position. Ongoing technological improvements and emerging techniques will enhance lung perfusion imaging and aid its incorporation into the care of patients with ARDS.
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Affiliation(s)
- Timothy G Gaulton
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA; Department of Anesthesia, Critical Care and Pain Medicine, Harvard Medical School, Boston, MA, USA.
| | - Yi Xin
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA; Department of Anesthesia, Critical Care and Pain Medicine, Harvard Medical School, Boston, MA, USA
| | - Marcus Victor
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA; Department of Anesthesia, Critical Care and Pain Medicine, Harvard Medical School, Boston, MA, USA; Electronics Engineering Division, Aeronautics Institute of Technology, Sao Paulo, Brazil
| | - Alice Nova
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA; Department of Anesthesia, Critical Care and Pain Medicine, Harvard Medical School, Boston, MA, USA
| | - Maurizio Cereda
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA; Department of Anesthesia, Critical Care and Pain Medicine, Harvard Medical School, Boston, MA, USA
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4
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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, Iglesias JE, 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. [PMID: 38738750 DOI: 10.1002/ana.26954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [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.
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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, MA, USA
| | - Jennifer Guo
- Department of Neurology and the Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 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, CT, USA
| | - Gordon Sze
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Nandor K Pinter
- Department of Radiology, Jacobs School of Medicine & Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA
- Department of Neurosurgery, Jacobs School of Medicine & Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA
| | - Vinay Jaikumar
- Department of Neurosurgery, Jacobs School of Medicine & Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA
| | - Adnan Siddiqui
- Department of Neurosurgery, Jacobs School of Medicine & Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA
| | - Steven Baldassano
- Division of Neuroradiology, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Ana-Lucia Garcia-Guarniz
- Department of Neurology and the Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 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, MA, USA
| | - Olivia K Nelson
- Department of Emergency Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Pamela W Schaefer
- Division of Neuroradiology, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Charles R Wira
- Department of Emergency Medicine, Yale New Haven Hospital and Yale School of Medicine, New Haven, CT, USA
| | - John Pitts
- Hyperfine Incorporated, Guilford, CT, USA
| | - Vivien Lee
- Wexner Medical Center, Ohio State University, Columbus, OH, USA
| | - Keith W Muir
- School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
| | - Shahid M Nimjee
- Wexner Medical Center, Ohio State University, Columbus, OH, USA
| | - John Kirsch
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Juan Eugenio Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Center 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 and Harvard Medical School, Boston, MA, 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, MA, USA
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5
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Seghier ML, Maalej N. Rescue equipment should include portable medical imaging systems. Sci Bull (Beijing) 2024:S2095-9273(24)00315-3. [PMID: 38755086 DOI: 10.1016/j.scib.2024.04.066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2024]
Affiliation(s)
- Mohamed L Seghier
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab Emirates; Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab Emirates.
| | - Nabil Maalej
- Physics Department, Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab Emirates.
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Wan C, He W, Xu Z. Water-Fat Separation for the Knee on a 50 mT Portable MRI Scanner. IEEE Trans Biomed Eng 2024; 71:1687-1696. [PMID: 38150336 DOI: 10.1109/tbme.2023.3347441] [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: 12/29/2023]
Abstract
OBJECTIVE The Dixon method is frequently employed in clinical and scientific research for fat suppression, because it has lower sensitivity to static magnetic field inhomogeneity compared to chemical shift selective saturation or its variants and maintains image signal-to-noise ratio (SNR). Recently, research on very-low-field (VLF < 100 mT) magnetic resonance imaging (MRI) has regained popularity. However, there is limited literature on water-fat separation in VLF MRI. Here, we present a modified two-point Dixon method specifically designed for VLF MRI. METHODS Most experiments were performed on a homemade 50 mT portable MRI scanner. The receiving coil adopted a homemade quadrature receiving coil. The data were acquired using spin-echo and gradient-echo sequences. We considered the T2* effect, and added priori information to existing two-point Dixon method. Then, the method used regional iterative phasor extraction (RIPE) to extract the error phasor. Finally, least squares solutions for water and fat were obtained and fat signal fraction was calculated. RESULTS For phantom evaluation, water-only and fat-only images were obtained and the local fat signal fractions were calculated, with two samples being 0.94 and 0.93, respectively. For knee imaging, cartilage, muscle and fat could be clearly distinguished. The water-only images were able to highlight areas such as cartilage that could not be easily distinguished without separation. CONCLUSION This work has demonstrated the feasibility of using a 50 mT MRI scanner for water-fat separation. SIGNIFICANCE To the best of our knowledge, this is the first reported result of water-fat separation at a 50 mT portable MRI scanner.
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7
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de Vos B, Remis R, Webb A. Segmented RF shield design to minimize eddy currents for low-field Halbach MRI systems. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2024; 362:107669. [PMID: 38598991 DOI: 10.1016/j.jmr.2024.107669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 03/29/2024] [Accepted: 04/03/2024] [Indexed: 04/12/2024]
Abstract
MRI systems have a thin conducting layer placed between the gradient and RF coils, this acts as a shield at the RF-frequency, minimizing noise coupled into the experiment, and decreasing the coupling between the RF and gradient coils. Ideally, this layer should be transparent to the gradient fields to reduce eddy currents. In this work the design of such a shield, specifically for low-field point-of-care Halbach based MRI devices, is discussed. A segmented double layer shield is designed and constructed based on eddy current simulations. Subsequently, the performance of the improved shield is compared to a reference shield by measuring the eddy current decay times as well as using noise measurements. A maximum reduction factor of 2.9 in the eddy current decay time is observed. The segmented shield couples in an equivalent amount of noise when compared to the unsegmented reference shield. Turbo spin echo images of a phantom and the brain of a healthy volunteer show improvements in terms of blurring using the segmented shield.
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Affiliation(s)
- Bart de Vos
- C.J. Gorter MRI center, Radiology, Leiden University Center, Leiden, The Netherlands; Terahertz Sensing, Microelectronics, Delft University of Technology, Delft, The Netherlands.
| | - Rob Remis
- Terahertz Sensing, Microelectronics, Delft University of Technology, Delft, The Netherlands
| | - Andrew Webb
- C.J. Gorter MRI center, Radiology, Leiden University Center, Leiden, The Netherlands; Terahertz Sensing, Microelectronics, Delft University of Technology, Delft, The Netherlands
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8
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Qiu Y, Dai K, Zhong S, Chen S, Wang C, Chen H, Frydman L, Zhang Z. Spatiotemporal encoding MRI in a portable low-field system. Magn Reson Med 2024. [PMID: 38623991 DOI: 10.1002/mrm.30104] [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: 12/05/2023] [Revised: 03/18/2024] [Accepted: 03/19/2024] [Indexed: 04/17/2024]
Abstract
PURPOSE Demonstrate the potential of spatiotemporal encoding (SPEN) MRI to deliver largely undistorted 2D, 3D, and diffusion weighted images on a 110 mT portable system. METHODS SPEN's quadratic phase modulation was used to subsample the low-bandwidth dimension of echo planar acquisitions, delivering alias-free images with an enhanced immunity to image distortions in a laboratory-built, low-field, portable MRI system lacking multiple receivers. RESULTS Healthy brain images with different SPEN time-bandwidth products and subsampling factors were collected. These compared favorably to EPI acquisitions including topup corrections. Robust 3D and diffusion weighted SPEN images of diagnostic value were demonstrated, with 2.5 mm isotropic resolutions achieved in 3 min scans. This performance took advantage of the low specific absorption rate and relative long TEs associated with low-field MRI. CONCLUSION SPEN MRI provides a robust and advantageous fast acquisition approach to obtain faithful 3D images and DWI data in low-cost, portable, low-field systems without parallel acceleration.
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Affiliation(s)
- Yueqi Qiu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT), Shanghai Jiao Tong University, Shanghai, People's Republic of China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Ke Dai
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT), Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Sijie Zhong
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT), Shanghai Jiao Tong University, Shanghai, People's Republic of China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Suen Chen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT), Shanghai Jiao Tong University, Shanghai, People's Republic of China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Changyue Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Hao Chen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT), Shanghai Jiao Tong University, Shanghai, People's Republic of China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Lucio Frydman
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, Israel
| | - Zhiyong Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT), Shanghai Jiao Tong University, Shanghai, People's Republic of China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China
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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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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] [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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Gong Z, Zeng L, Jiang B, Zhu R, Wang J, Li M, Shao A, Lv Z, Zhang M, Guo L, Li G, Sun J, Chen Y. Dynamic cerebral blood flow assessment based on electromagnetic coupling sensing and image feature analysis. Front Bioeng Biotechnol 2024; 12:1276795. [PMID: 38449677 PMCID: PMC10915240 DOI: 10.3389/fbioe.2024.1276795] [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: 08/13/2023] [Accepted: 02/07/2024] [Indexed: 03/08/2024] Open
Abstract
Dynamic assessment of cerebral blood flow (CBF) is crucial for guiding personalized management and treatment strategies, and improving the prognosis of stroke. However, a safe, reliable, and effective method for dynamic CBF evaluation is currently lacking in clinical practice. In this study, we developed a CBF monitoring system utilizing electromagnetic coupling sensing (ECS). This system detects variations in brain conductivity and dielectric constant by identifying the resonant frequency (RF) in an equivalent circuit containing both magnetic induction and electrical coupling. We evaluated the performance of the system using a self-made physical model of blood vessel pulsation to test pulsatile CBF. Additionally, we recruited 29 healthy volunteers to monitor cerebral oxygen (CO), cerebral blood flow velocity (CBFV) data and RF data before and after caffeine consumption. We analyzed RF and CBFV trends during immediate responses to abnormal intracranial blood supply, induced by changes in vascular stiffness, and compared them with CO data. Furthermore, we explored a method of dynamically assessing the overall level of CBF by leveraging image feature analysis. Experimental testing substantiates that this system provides a detection range and depth enhanced by three to four times compared to conventional electromagnetic detection techniques, thereby comprehensively covering the principal intracranial blood supply areas. And the system effectively captures CBF responses under different intravascular pressure stimulations. In healthy volunteers, as cerebral vascular stiffness increases and CO decreases due to caffeine intake, the RF pulsation amplitude diminishes progressively. Upon extraction and selection of image features, widely used machine learning algorithms exhibit commendable performance in classifying overall CBF levels. These results highlight that our proposed methodology, predicated on ECS and image feature analysis, enables the capture of immediate responses of abnormal intracranial blood supply triggered by alterations in vascular stiffness. Moreover, it provides an accurate diagnosis of the overall CBF level under varying physiological conditions.
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Affiliation(s)
- Zhiwei Gong
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, China
| | - Lingxi Zeng
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, China
| | - Bin Jiang
- College of Artificial Intelligence, Chongqing University of Technology, Chongqing, China
| | - Rui Zhu
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, China
| | - Junjie Wang
- College of Artificial Intelligence, Chongqing University of Technology, Chongqing, China
| | - Mingyan Li
- College of Artificial Intelligence, Chongqing University of Technology, Chongqing, China
| | - Ansheng Shao
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, China
| | - Zexiang Lv
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, China
| | - Maoting Zhang
- College of Biomedical Engineering, Army Medical University, Chongqing, China
| | - Lei Guo
- School of Information and Communication Engineering, Dalian University of Technology, Dalian, Liaoning, China
| | - Gen Li
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, China
- Department of Neurosurgery, Southwest Hospital, Army Medical University, Chongqing, China
| | - Jian Sun
- College of Biomedical Engineering, Army Medical University, Chongqing, China
| | - Yujie Chen
- Department of Neurosurgery, Southwest Hospital, Army Medical University, Chongqing, China
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12
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Selvaganesan K, Ha Y, Sun H, Zhang Z, Sun C, Samardzija A, Galiana G, Constable RT. Encoding scheme design for gradient-free, nonlinear projection imaging using Bloch-Siegert RF spatial encoding in a low-field, open MRI system. Sci Rep 2024; 14:3307. [PMID: 38332252 PMCID: PMC10853509 DOI: 10.1038/s41598-024-53703-y] [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: 07/10/2023] [Accepted: 02/03/2024] [Indexed: 02/10/2024] Open
Abstract
Eliminating conventional pulsed B0-gradient coils for magnetic resonance imaging (MRI) can significantly reduce the cost of and increase access to these devices. Phase shifts induced by the Bloch-Siegert shift effect have been proposed as a means for gradient-free, RF spatial encoding for low-field MR imaging. However, nonlinear phasor patterns like those generated from loop coils have not been systematically studied in the context of 2D spatial encoding. This work presents an optimization algorithm to select an efficient encoding trajectory among the nonlinear patterns achievable with a given hardware setup. Performance of encoding trajectories or projections was evaluated through simulated and experimental image reconstructions. Results show that the encodings schemes designed by this algorithm provide more efficient spatial encoding than comparison encoding sets, and the method produces images with the predicted spatial resolution and minimal artifacts. Overall, the work demonstrates the feasibility of performing 2D gradient-free, low-field imaging using the Bloch-Siegert shift which is an important step towards creating low-cost, point-of-care MR systems.
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Affiliation(s)
| | - Yonghyun Ha
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Heng Sun
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Zhehong Zhang
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Chenhao Sun
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Anja Samardzija
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Gigi Galiana
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - R Todd Constable
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
- Yale University School of Medicine, Magnetic Resonance Research Center, 300 Cedar Street, New Haven, CT, 06520, USA.
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13
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de Vos B, Remis RF, Webb AG. Characterization of concomitant gradient fields and their effects on image distortions using a low-field point-of-care Halbach-based MRI system. Magn Reson Med 2024; 91:828-841. [PMID: 37749850 DOI: 10.1002/mrm.29879] [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: 05/15/2023] [Revised: 09/07/2023] [Accepted: 09/08/2023] [Indexed: 09/27/2023]
Abstract
PURPOSE Concomitant gradient fields have been extensively studied at clinical field strengths. However, their effects have not yet been modeled for low-field point-of-care (POC) systems. The purpose of this work is to characterize the effects associated with concomitant fields for POC Halbach-array-based systems. METHODS The concomitant fields associated with a cylindrical gradient coils designed for a transverseB 0 $$ {B}_0 $$ and a signal model including the tilting effect of the effective magnetic field are derived. The formalism is used to simulate and predict concomitant field related distortions. A 46-mT Halbach-array-based system with a maximum gradient strength of 15 mT/m is used to verify the model using two-dimensional spin-echo sequences. RESULTS The simulations and experimental results are in good agreement with the derived equations. The fundamental characteristics of the concomitant field equations are different to conventional MRI systems: Image distortions occur primarily in the transverse directions and a cross-term only exists when applying transverse gradient pulses simultaneously. CONCLUSION The level of image warping in the frequency encoding direction is insignificant for the POC systems discussed here. However, when trying to achieve short echo-times by using strong phase encoding and readout-dephasing gradients, the combination can result in image warping and blurring which should be accounted for in image interpretation.
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Affiliation(s)
- Bart de Vos
- Radiology, C.J. Gorter MRI Center, Leiden University Medical Center, Leiden, The Netherlands
| | - Rob F Remis
- Micro-electronics, Signal Processing Systems, Delft University of Technology, Delft, The Netherlands
| | - Andrew G Webb
- Radiology, C.J. Gorter MRI Center, Leiden University Medical Center, Leiden, The Netherlands
- Micro-electronics, Terahertz Sensing, Delft University of Technology, Delft, The Netherlands
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14
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Zalis ME, Slutzman JE. Technical and Administrative Advances to Promote Sustainable Radiology. J Am Coll Radiol 2024; 21:274-279. [PMID: 38048966 DOI: 10.1016/j.jacr.2023.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 11/30/2023] [Accepted: 12/01/2023] [Indexed: 12/06/2023]
Abstract
Climate change mandates that we take steps to understand and mitigate the negative environmental consequences of the practice of health care, so that health care advances sustainably. In this article, the authors review and discuss a sample of technical and administrative advances required to align the practice of radiology with principles of environmental sustainability.
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Affiliation(s)
- Michael E Zalis
- Director, Mass General Brigham Radiology Center for Sustainability, Boston, Massachusetts; Divisions of Cardiovascular and Interventional Radiology, Department of Radiology, Mass General Hospital, Boston, Massachusetts.
| | - Jonathan E Slutzman
- Director, Mass General Center for the Environment and Health, Massachusetts General Hospital, Boston, Massachusetts; Department of Emergency Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
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15
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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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16
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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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17
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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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18
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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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19
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Bardwell Speltz LJ, Shu Y, Watson RE, Trzasko JD, In MH, Gray EM, Halverson MA, Tarasek MR, Hua Y, Huston J, Cogswell PM, Foo TKF, Bernstein MA. Evaluation of a compact 3 T MRI scanner for patients with implanted devices. Magn Reson Imaging 2023; 103:109-118. [PMID: 37468020 PMCID: PMC10528046 DOI: 10.1016/j.mri.2023.07.009] [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: 05/15/2023] [Revised: 07/14/2023] [Accepted: 07/14/2023] [Indexed: 07/21/2023]
Abstract
Access to high-quality MR exams is severely limited for patients with some implanted devices due to labeled MR safety conditions, but small-bore systems can overcome this limitation. For example, a compact 3 T MR scanner (C3T) with high-performance gradients can acquire exams of the head, extremities, and infants. Because of its reduced bore size and the patient being advanced only partially into the bore, the associated electromagnetic (EM) fields drop off rapidly caudal to the head, compared to whole-body systems. Therefore, some patients with MR conditional implanted devices can safely receive 3 T brain exams on the C3T using its strong gradients and a multiple-channel receive coil, while a corresponding exam on whole-body MR is precluded. The purpose of this study is to evaluate the performance of a small-bore scanner for subjects with MR conditional spinal or sacral nerve stimulators, or abandoned cardiac implantable electronic device (CIED) leads. The spatial dependence of specific absorption rate (SAR) on the C3T was compared to whole-body scanners. A device assessment tool was developed and applied to evaluate MR safety individually on the C3T for 12 subjects with implanted devices or abandoned CIED leads. Once MR safety was established, the subjects received a C3T brain exam along with their clinical, 1.5 T exam. The resulting images were graded by three board-certified neuroradiologists. The C3T exams were well-tolerated with no adverse events, and significantly outperformed the whole-body 1.5 T exams in terms of overall image quality.
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Affiliation(s)
- Lydia J Bardwell Speltz
- Department of Radiology, Mayo Clinic, Rochester, MN, United States; Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, Rochester, MN, United States
| | - Yunhong Shu
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Robert E Watson
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Joshua D Trzasko
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Myung-Ho In
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Erin M Gray
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | | | | | - Yihe Hua
- GE Research, Niskayuna, NY, United States
| | - John Huston
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | | | | | - Matt A Bernstein
- Department of Radiology, Mayo Clinic, Rochester, MN, United States.
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20
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Cawley P, Padormo F, Cromb D, Almalbis J, Marenzana M, Teixeira R, Uus A, O’Muircheartaigh J, Williams SC, Counsell SJ, Arichi T, Rutherford MA, Hajnal JV, Edwards AD. Development of neonatal-specific sequences for portable ultralow field magnetic resonance brain imaging: a prospective, single-centre, cohort study. EClinicalMedicine 2023; 65:102253. [PMID: 38106560 PMCID: PMC10725077 DOI: 10.1016/j.eclinm.2023.102253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 09/14/2023] [Accepted: 09/15/2023] [Indexed: 12/19/2023] Open
Abstract
Background Magnetic Resonance (MR) imaging is key for investigation of suspected newborn brain abnormalities. Access is limited in low-resource settings and challenging in infants needing intensive care. Portable ultralow field (ULF) MRI is showing promise in bedside adult brain imaging. Use in infants and children has been limited as brain-tissue composition differences necessitate sequence modification. The aim of this study was to develop neonatal-specific ULF structural sequences and test these across a range of gestational maturities and pathologies to inform future validation studies. Methods Prospective cohort study within a UK neonatal specialist referral centre. Infants undergoing 3T MRI were recruited for paired ULF (64mT) portable MRI by convenience sampling from the neonatal unit and post-natal ward. Key inclusion criteria: 1) Infants with risk or suspicion of brain abnormality, or 2) preterm and term infants without suspicion of major genetic, chromosomal or neurological abnormality. Exclusions: presence of contra-indication for MR scanning. ULF sequence parameters were optimised for neonatal brain-tissues by iterative and explorative design. Neuroanatomic and pathologic features were compared by unblinded review, informing optimisation of subsequent sequence generations in a step-wise manner. Main outcome: visual identification of healthy and abnormal brain tissues/structures. ULF MR spectroscopy, diffusion, susceptibility weighted imaging, arteriography, and venography require pre-clinical technical development and have not been tested. Findings Between September 23, 2021 and October 25, 2022, 102 paired scans were acquired in 87 infants; 1.17 paired scans per infant. Median age 9 days, median postmenstrual age 40+2 weeks (range: 31+3-53+4). Infants had a range of intensive care requirements. No adverse events observed. Optimised ULF sequences can visualise key neuroanatomy and brain abnormalities. In finalised neonatal sequences: T2w imaging distinguished grey and white matter (7/7 infants), ventricles (7/7), pituitary tissue (5/7), corpus callosum (7/7) and optic nerves (7/7). Signal congruence was seen within the posterior limb of the internal capsule in 10/11 infants on finalised T1w scans. In addition, brain abnormalities visualised on ULF optimised sequences have similar MR signal patterns to 3T imaging, including injury secondary to infarction (6/6 infants on T2w scans), hypoxia-ischaemia (abnormal signal in basal ganglia, thalami and white matter 2/2 infants on T2w scans, cortical highlighting 1/1 infant on T1w scan), and congenital malformations: polymicrogyria 3/3, absent corpus callosum 2/2, and vermian hypoplasia 3/3 infants on T2w scans. Sequences are susceptible to motion corruption, noise, and ULF artefact. Non-identified pathologies were small or subtle. Interpretation On unblinded review, optimised portable MR can provide sufficient contrast, signal, and resolution for neuroanatomical identification and detection of a range of clinically important abnormalities. Blinded validation studies are now warranted. Funding The Bill and Melinda Gates Foundation, the MRC, the Wellcome/EPSRC Centre for Medical Engineering, the MRC Centre for Neurodevelopmental Disorders, and the National Institute for Health Research (NIHR) Biomedical Research Centres based at Guy's and St Thomas' and South London & Maudsley NHS Foundation Trusts and King's College London.
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Affiliation(s)
- Paul Cawley
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
- Neonatal Intensive Care Unit, Evelina Children’s Hospital London, St Thomas’ Hospital, 6th Floor North Wing, Westminster Bridge Road, London SE1 7EH, UK
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London SE1 1UL, UK
| | - Francesco Padormo
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
- Medical Physics, Guy’s & St. Thomas' NHS Foundation Trust, London, UK
- Hyperfine, Inc., 351 New Whitfield St., Guilford, Connecticut 06437, USA
| | - Daniel Cromb
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
- Neonatal Intensive Care Unit, Evelina Children’s Hospital London, St Thomas’ Hospital, 6th Floor North Wing, Westminster Bridge Road, London SE1 7EH, UK
| | - Jennifer Almalbis
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
- Neonatal Intensive Care Unit, Evelina Children’s Hospital London, St Thomas’ Hospital, 6th Floor North Wing, Westminster Bridge Road, London SE1 7EH, UK
| | - Massimo Marenzana
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Rui Teixeira
- Hyperfine, Inc., 351 New Whitfield St., Guilford, Connecticut 06437, USA
| | - Alena Uus
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Jonathan O’Muircheartaigh
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London SE1 1UL, UK
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Steven C.R. Williams
- Centre for Neuroimaging Sciences, King’s College London, De Crespigny Park, London SE5 8AF, UK
| | - Serena J. Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Tomoki Arichi
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London SE1 1UL, UK
- Paediatric Neurosciences, Evelina London Children’s Hospital, Guy’s and St Thomas’ NHS Foundation Trust, London SE1 7EH, UK
| | - Mary A. Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London SE1 1UL, UK
| | - Joseph V. Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - A. David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
- Neonatal Intensive Care Unit, Evelina Children’s Hospital London, St Thomas’ Hospital, 6th Floor North Wing, Westminster Bridge Road, London SE1 7EH, UK
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London SE1 1UL, UK
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21
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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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22
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张 宇, 何 为, 杨 磊, 贺 玉, 吴 嘉, 徐 征. [Research on a portable shielding-free ultra-low field magnetic resonance imaging system]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2023; 40:829-836. [PMID: 37879910 PMCID: PMC10600425 DOI: 10.7507/1001-5515.202303060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 08/30/2023] [Indexed: 10/27/2023]
Abstract
The portable light-weight magnetic resonance imaging system can be deployed in special occasions such as Intensive Care Unit (ICU) and ambulances, making it possible to implement bedside monitoring imaging systems, mobile stroke units and magnetic resonance platforms in remote areas. Compared with medium and high field imaging systems, ultra-low-field magnetic resonance imaging equipment utilizes light-weight permanent magnets, which are compact and easy to move. However, the image quality is highly susceptible to external electromagnetic interference without a shielded room and there are still many key technical problems in hardware design to be solved. In this paper, the system hardware design and environmental electromagnetic interference elimination algorithm were studied. Consequently, some research results were obtained and a prototype of portable shielding-free 50 mT magnetic resonance imaging system was built. The light-weight magnet and its uniformity, coil system and noise elimination algorithm and human brain imaging were verified. Finally, high-quality images of the healthy human brain were obtained. The results of this study would provide reference for the development and application of ultra-low-field magnetic resonance imaging technology.
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Affiliation(s)
- 宇翔 张
- 重庆大学 电气工程学院(重庆 400044)School of Electrical Engineering, Chongqing University, Chongqing 400044, P. R. China
| | - 为 何
- 重庆大学 电气工程学院(重庆 400044)School of Electrical Engineering, Chongqing University, Chongqing 400044, P. R. China
| | - 磊 杨
- 重庆大学 电气工程学院(重庆 400044)School of Electrical Engineering, Chongqing University, Chongqing 400044, P. R. China
| | - 玉成 贺
- 重庆大学 电气工程学院(重庆 400044)School of Electrical Engineering, Chongqing University, Chongqing 400044, P. R. China
| | - 嘉敏 吴
- 重庆大学 电气工程学院(重庆 400044)School of Electrical Engineering, Chongqing University, Chongqing 400044, P. R. China
| | - 征 徐
- 重庆大学 电气工程学院(重庆 400044)School of Electrical Engineering, Chongqing University, Chongqing 400044, P. R. China
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23
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Akbari B, Huber BR, Sherman JH. Unlocking the Hidden Depths: Multi-Modal Integration of Imaging Mass Spectrometry-Based and Molecular Imaging Techniques. Crit Rev Anal Chem 2023:1-30. [PMID: 37847593 DOI: 10.1080/10408347.2023.2266838] [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: 10/19/2023]
Abstract
Multimodal imaging (MMI) has emerged as a powerful tool in clinical research, combining different imaging modes to acquire comprehensive information and enabling scientists and surgeons to study tissue identification, localization, metabolic activity, and molecular discovery, thus aiding in disease progression analysis. While multimodal instruments are gaining popularity, challenges such as non-standardized characteristics, custom software, inadequate commercial support, and integration issues with other instruments need to be addressed. The field of multimodal imaging or multiplexed imaging allows for simultaneous signal reproduction from multiple imaging strategies. Intraoperatively, MMI can be integrated into frameless stereotactic surgery. Recent developments in medical imaging modalities such as magnetic resonance imaging (MRI), and Positron Emission Topography (PET) have brought new perspectives to multimodal imaging, enabling early cancer detection, molecular tracking, and real-time progression monitoring. Despite the evidence supporting the role of MMI in surgical decision-making, there is a need for comprehensive studies to validate and perform integration at the intersection of multiple imaging technologies. They were integrating mass spectrometry-based technologies (e.g., imaging mass spectrometry (IMS), imaging mass cytometry (IMC), and Ion mobility mass spectrometry ((IM-IM) with medical imaging modalities, offering promising avenues for molecular discovery and clinical applications. This review emphasizes the potential of multi-omics approaches in tissue mapping using MMI integrated into desorption electrospray ionization (DESI) and matrix-assisted laser desorption ionization (MALDI), allowing for sequential analyses of the same section. By addressing existing knowledge gaps, this review encourages future research endeavors toward multi-omics approaches, providing a roadmap for future research and enhancing the value of MMI in molecular pathology for diagnosis.
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Affiliation(s)
- Behnaz Akbari
- Department of Chemistry, Purdue University, West Lafayette, Indiana, USA
| | - Bertrand Russell Huber
- Chobanian and Avedisian School of Medicine, Boston University, Boston, Massachusetts, USA
- Boston University Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, Massachusetts, USA
- Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, USA
- US Department of Veteran Affairs, VA Boston Healthcare System, Boston, Massachusetts USA
- US Department of Veterans Affairs, National Center for PTSD, Boston, Massachusetts USA
| | - Janet Hope Sherman
- Chobanian and Avedisian School of Medicine, Boston University, Boston, Massachusetts, USA
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24
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Rodrigue AL, Hayes RA, Waite E, Corcoran M, Glahn DC, Jalbrzikowski M. Multimodal Neuroimaging Summary Scores as Neurobiological Markers of Psychosis. Schizophr Bull 2023:sbad149. [PMID: 37844289 DOI: 10.1093/schbul/sbad149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/18/2023]
Abstract
BACKGROUND AND HYPOTHESIS Structural brain alterations are well-established features of schizophrenia but they do not effectively predict disease/disease risk. Similar to polygenic risk scores in genetics, we integrated multifactorial aspects of brain structure into a summary "Neuroscore" and examined its potential as a marker of disease. STUDY DESIGN We extracted measures from T1-weighted scans and diffusion tensor imaging (DTI) models from three studies with schizophrenia and healthy individuals. We calculated individual-level summary scores (Neuroscores) for T1-weighted and DTI measures and a combined score (Multimodal Neuroscore-MM). We assessed each score's ability to differentiate schizophrenia cases from controls and its relationship to clinical symptomatology, intelligence quotient (IQ), and medication dosage. We assessed Neuroscore specificity by performing all analyses in a more inclusive psychosis sample and by using scores generated from MDD effect sizes. STUDY RESULTS All Neuroscores significantly differentiated schizophrenia cases from controls (T1 d = 0.56, DTI d = 0.29, MM d = 0.64) to a greater degree than individual brain regions. Higher Neuroscores (ie, increased liability) were associated with lower IQ (T1 β = -0.26, DTI β = -0.15, MM β = -0.30). Higher T1-weighted Neuroscores were associated with higher positive and negative symptom severity (Positive β = 0.21, Negative β = 0.16); Higher Multimodal Neuroscores were associated with higher positive symptom severity (β = 0.30). SZ Neuroscores outperformed MDD Neuroscores in predicting IQ (T1: z = 3.5, q = 0.0007; MM: z = 1.8, q = 0.05). CONCLUSIONS Neuroscores are a step toward leveraging widespread structural brain alterations in psychosis to identify robust neurobiological markers of disease. Future studies will assess ways to improve neuroscore calculation, including developing the optimal methods to calculate neuroscores and considering disorder overlap.
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Affiliation(s)
- Amanda L Rodrigue
- Department of Psychiatry, Boston Children's Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Rebecca A Hayes
- Department of Psychiatry, Boston Children's Hospital, Boston, MA, USA
| | - Emma Waite
- Department of Psychiatry, Boston Children's Hospital, Boston, MA, USA
| | - Mary Corcoran
- Department of Psychiatry, Boston Children's Hospital, Boston, MA, USA
| | - David C Glahn
- Department of Psychiatry, Boston Children's Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
| | - Maria Jalbrzikowski
- Department of Psychiatry, Boston Children's Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
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25
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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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26
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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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27
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Zhang Y, He W, Yang L, Xuan L, Wu J, He Y, Guo Y, Xu Z. Efficient imaging using spiral acquisitions on a portable 50-mT MR head scanner. NMR IN BIOMEDICINE 2023; 36:e4988. [PMID: 37381057 DOI: 10.1002/nbm.4988] [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: 03/13/2023] [Revised: 05/21/2023] [Accepted: 05/22/2023] [Indexed: 06/30/2023]
Abstract
Ultralow-field (ULF) magnetic resonance imaging (MRI) can suffer from inferior image quality because of low signal-to-noise ratio (SNR). As an efficient way to cover the k-space, the spiral acquisition technique has shown great potential in improving imaging SNR efficiency at ULF. The current study aimed to address the problems of noise and blurring cancelation in the ULF case with spiral trajectory, and we proposed a spiral-out sequence for brain imaging using a portable 50-mT MRI system. The proposed sequence consisted of three modules: noise calibration, field map acquisition, and imaging. In the calibration step, transfer coefficients were obtained between signals from primary and noise-pick-up coils to perform electromagnetic interference (EMI) cancelation. Embedded field map acquisition was performed to correct accumulated phase error due to main field inhomogeneity. Considering imaging SNR, a lower bandwidth for data sampling was adopted in the sequence design because the 50-mT scanner is in a low SNR regime. Image reconstruction proceeded with sampled data by leveraging system imperfections, such as gradient delays and concomitant fields. The proposed method can provide images with higher SNR efficiency compared with its Cartesian counterparts. An improvement in temporal SNR of approximately 23%-44% was measured via phantom and in vivo experiments. Distortion-free images with a noise suppression rate of nearly 80% were obtained by the proposed technique. A comparison was also made with a state-of-the-art EMI cancelation algorithm used in the ULF-MRI system. SNR efficiency-enhanced spiral acquisitions were investigated for ULF-MR scanners and future studies could focus on various image contrasts based on our proposed approach to widen ULF applications.
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Affiliation(s)
- Yuxiang Zhang
- School of Electrical Engineering, Chongqing University, Chongqing, China
| | - Wei He
- School of Electrical Engineering, Chongqing University, Chongqing, China
| | - Lei Yang
- School of Electrical Engineering, Chongqing University, Chongqing, China
| | - Liang Xuan
- School of Electrical Engineering, Chongqing University, Chongqing, China
| | - Jiamin Wu
- Shenzhen Academy of Aerospace Technology, Shenzhen, China
- Harbin Institute of Technology, Harbin, China
| | - Yucheng He
- Shenzhen Academy of Aerospace Technology, Shenzhen, China
| | - Yi Guo
- Chongqing University Central Hospital, Chongqing, China
| | - Zheng Xu
- School of Electrical Engineering, Chongqing University, Chongqing, China
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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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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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Islam O, Lin AW, Bharatha A. Potential application of ultra-low field portable MRI in the ICU to improve CT and MRI access in Canadian hospitals: a multi-center retrospective analysis. Front Neurol 2023; 14:1220091. [PMID: 37808492 PMCID: PMC10551136 DOI: 10.3389/fneur.2023.1220091] [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: 05/10/2023] [Accepted: 08/28/2023] [Indexed: 10/10/2023] Open
Abstract
Background To highlight the value of Portable MRI in ICU and to recommend use case scenarios for portable MRI in ICU patients that may increase capacity for fixed CT and MRI units. Urgent neuroimaging is commonly required in ICU. Typically, ICU patients are transported to Radiology for assessment in fixed CT and MRI units. Portable MRI use in Canadian ICU settings offers the potential advantages of reduced transport risk, earlier diagnosis, improved triaging, as well as the ability to perform frequent re-imaging at the bedside. This frees up time on fixed CT and MRI units, leading to enhanced capacity to perform CT and MRI on other patients. Portable MRI use case scenarios in Canadian institutions have not been established and potential beneficial effect on wait times has not been analyzed. Methods A retrospective semi-quantitative descriptive analysis was performed using all ICU neuroimaging requisitions (CT and MRI) over a 12-month period between January and December 2021, at Kingston Health Sciences Centre, Queen's University (Kingston, Ontario) and St. Michael's Hospital, Unity Health, University of Toronto (Toronto, Ontario). Indications for portable MRI in ICU patients were established. The number of ICU patients who could potentially undergo portable MRI was determined. Fixed CT and MRI scan times saved were calculated. Results In ICU patients, portable MRI could potentially replace fixed CT in 21% and fixed MRI in 26.5% of cases. This equates to annual capacity increase of 1,676 additional patients being able to undergo fixed CT scans and 324 additional patients being able to undergo fixed MRI. Conclusion Implementation of portable MRI in the ICU for select neurological indications can have a significant positive impact on CT and MRI wait times in Canadian hospitals.
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Affiliation(s)
- Omar Islam
- Department of Neuroradiology, Kingston Health Sciences Centre, Queen’s University, Kingston, ON, Canada
| | - Amy W. Lin
- Department of Medical Imaging, St. Michael’s Hospital, Unity Health Toronto, University of Toronto, Toronto, ON, Canada
| | - Aditya Bharatha
- Department of Medical Imaging, St. Michael’s Hospital, Unity Health Toronto, University of Toronto, Toronto, ON, Canada
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Tu D, Goyal MS, Dworkin JD, Kampondeni S, Vidal L, Biondo-Savin E, Juvvadi S, Raghavan P, Nicholas J, Chetcuti K, Clark K, Robert-Fitzgerald T, Satterthwaite TD, Yushkevich P, Davatzikos C, Erus G, Tustison NJ, Postels DG, Taylor TE, Small DS, Shinohara RT. Automated analysis of low-field brain MRI in cerebral malaria. Biometrics 2023; 79:2417-2429. [PMID: 35731973 PMCID: PMC10267853 DOI: 10.1111/biom.13708] [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: 03/06/2021] [Accepted: 06/08/2022] [Indexed: 11/26/2022]
Abstract
A central challenge of medical imaging studies is to extract biomarkers that characterize disease pathology or outcomes. Modern automated approaches have found tremendous success in high-resolution, high-quality magnetic resonance images. These methods, however, may not translate to low-resolution images acquired on magnetic resonance imaging (MRI) scanners with lower magnetic field strength. In low-resource settings where low-field scanners are more common and there is a shortage of radiologists to manually interpret MRI scans, it is critical to develop automated methods that can augment or replace manual interpretation, while accommodating reduced image quality. We present a fully automated framework for translating radiological diagnostic criteria into image-based biomarkers, inspired by a project in which children with cerebral malaria (CM) were imaged using low-field 0.35 Tesla MRI. We integrate multiatlas label fusion, which leverages high-resolution images from another sample as prior spatial information, with parametric Gaussian hidden Markov models based on image intensities, to create a robust method for determining ventricular cerebrospinal fluid volume. We also propose normalized image intensity and texture measurements to determine the loss of gray-to-white matter tissue differentiation and sulcal effacement. These integrated biomarkers have excellent classification performance for determining severe brain swelling due to CM.
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Affiliation(s)
- Danni Tu
- The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania
| | - Manu S. Goyal
- Mallinckrodt Institute of Radiology, Washington University in St. Louis
| | | | | | - Lorenna Vidal
- Department of Radiology, Children’s Hospital of Philadelphia
| | | | | | - Prashant Raghavan
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine
| | - Jennifer Nicholas
- University Hospitals Cleveland Medical Center, Department of Radiology, Case Western Reserve University
| | - Karen Chetcuti
- Department of Paediatrics and Child Health, Kamuzu University of Health Sciences
| | - Kelly Clark
- The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania
| | - Timothy Robert-Fitzgerald
- The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania
| | | | | | | | - Guray Erus
- Center for Biomedical Image Computing and Analysis (CBICA), Department of Radiology, University of Pennsylvania
| | | | - Douglas G. Postels
- Division of Neurology, George Washington University/Children’s National Medical Center
| | - Terrie E. Taylor
- Blantyre Malaria Project, Kamuzu University of Health Sciences
- College of Osteopathic Medicine, Michigan State University
| | | | - Russell T. Shinohara
- The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania
- Center for Biomedical Image Computing and Analysis (CBICA), Department of Radiology, University of Pennsylvania
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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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Bossert S, Unadkat P, Sheth KN, Sze G, Schulder M. A Novel Portable, Mobile MRI: Comparison with an Established Low-Field Intraoperative MRI System. Asian J Neurosurg 2023; 18:492-498. [PMID: 38152522 PMCID: PMC10749856 DOI: 10.1055/s-0043-1760857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2023] Open
Abstract
Background MRI (magnetic resonance imaging) using low-magnet field strength has unique advantages for intraoperative use. We compared a novel, compact, portable MR imaging system to an established intraoperative 0.15 T system to assess potential utility in intracranial neurosurgery. Methods Brain images were acquired with a 0.15 T intraoperative MRI (iMRI) system and a 0.064 T portable MR system. Five healthy volunteers were scanned. Individual sequences were rated on a 5-point (1 to 5) scale for six categories: contrast, resolution, coverage, noise, artifacts, and geometry. Results Overall, the 0.064 T images (M = 3.4, SD = 0.1) had statistically higher ratings than the 0.15 T images (M = 2.4, SD = 0.2) ( p < 0.01). All comparable sequences (T1, T2, T2 FLAIR and SSFP) were rated significantly higher on the 0.064 T and were rated 1.2 points (SD = 0.3) higher than 0.15 T scanner, with the T2 fluid-attenuated inversion recovery (FLAIR) sequences showing the largest increment on the 0.064 T with an average rating difference of 1.5 points (SD = 0.2). Scanning time for the 0.064 T system obtained images more quickly and encompassed a larger field of view than the 0.15 T system. Conclusions A novel, portable 0.064 T self-shielding MRI system under ideal conditions provided images of comparable quality or better and faster acquisition times than those provided by the already well-established 0.15 T iMR system. These results suggest that the 0.064 T MRI has the potential to be adapted for intraoperative use for intracranial neurosurgery.
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Affiliation(s)
- Sharon Bossert
- Department of Neurosurgery, Zucker School of Medicine at Hofstra/Northwell Health, New York, United States
| | - Prashin Unadkat
- Department of Neurosurgery, Zucker School of Medicine at Hofstra/Northwell Health, New York, United States
- Elmezzi Graduate School of Molecular Medicine, Northwell Health, Manhasset, United States
| | - Kevin N Sheth
- Department of Neurology, Yale School of Medicine, New Haven, Connecticut, United States
| | - Gordon Sze
- Department of Radiology, Yale School of Medicine, New Haven, Connecticut, United States
| | - Michael Schulder
- Department of Neurosurgery, Zucker School of Medicine at Hofstra/Northwell Health, New York, United States
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Oberdick SD, Jordanova KV, Lundstrom JT, Parigi G, Poorman ME, Zabow G, Keenan KE. Iron oxide nanoparticles as positive T 1 contrast agents for low-field magnetic resonance imaging at 64 mT. Sci Rep 2023; 13:11520. [PMID: 37460669 DOI: 10.1038/s41598-023-38222-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 07/05/2023] [Indexed: 07/20/2023] Open
Abstract
We have investigated the efficacy of superparamagnetic iron oxide nanoparticles (SPIONs) as positive T1 contrast agents for low-field magnetic resonance imaging (MRI) at 64 millitesla (mT). Iron oxide-based agents, such as the FDA-approved ferumoxytol, were measured using a variety of techniques to evaluate T1 contrast at 64 mT. Additionally, we characterized monodispersed carboxylic acid-coated SPIONs with a range of diameters (4.9-15.7 nm) in order to understand size-dependent properties of T1 contrast at low-field. MRI contrast properties were measured using 64 mT MRI, magnetometry, and nuclear magnetic resonance dispersion (NMRD). We also measured MRI contrast at 3 T to provide comparison to a standard clinical field strength. SPIONs have the capacity to perform well as T1 contrast agents at 64 mT, with measured longitudinal relaxivity (r1) values of up to 67 L mmol-1 s-1, more than an order of magnitude higher than corresponding r1 values at 3 T. The particles exhibit size-dependent longitudinal relaxivities and outperform a commercial Gd-based agent (gadobenate dimeglumine) by more than eight-fold at physiological temperatures. Additionally, we characterize the ratio of transverse to longitudinal relaxivity, r2/r1 and find that it is ~ 1 for the SPION based agents at 64 mT, indicating a favorable balance of relaxivities for T1-weighted contrast imaging. We also correlate the magnetic and structural properties of the particles with models of nanoparticle relaxivity to understand generation of T1 contrast. These experiments show that SPIONs, at low fields being targeted for point-of-care low-field MRI systems, have a unique combination of magnetic and structural properties that produce large T1 relaxivities.
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Affiliation(s)
- Samuel D Oberdick
- Department of Physics, University of Colorado, Boulder, CO, 80309, USA.
- National Institute of Standards and Technology, Boulder, CO, 80305, USA.
| | | | - John T Lundstrom
- Department of Physics, University of Colorado, Boulder, CO, 80309, USA
- National Institute of Standards and Technology, Boulder, CO, 80305, USA
| | - Giacomo Parigi
- Magnetic Resonance Center (CERM), University of Florence, Via Luigi Sacconi 6, 50019, Sesto Fiorentino, Italy
- Department of Chemistry "Ugo Schiff", University of Florence, Via Della Lastruccia 3, 50019, Sesto Fiorentino, Italy
- Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), Via Luigi Sacconi 6, 50019, Sesto Fiorentino, Italy
| | | | - Gary Zabow
- National Institute of Standards and Technology, Boulder, CO, 80305, USA
| | - Kathryn E Keenan
- National Institute of Standards and Technology, Boulder, CO, 80305, USA
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Abbas A, Hilal K, Rasool AA, Zahidi UF, Shamim MS, Abbas Q. Low-field magnetic resonance imaging in a boy with intracranial bolt after severe traumatic brain injury: illustrative case. JOURNAL OF NEUROSURGERY. CASE LESSONS 2023; 6:CASE23225. [PMID: 37392768 PMCID: PMC10555635 DOI: 10.3171/case23225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 05/24/2023] [Indexed: 07/03/2023]
Abstract
BACKGROUND Conventional magnetic resonance imaging (cMRI) is sensitive to motion and ferromagnetic material, leading to suboptimal images and image artifacts. In many patients with neurological injuries, an intracranial bolt (ICB) is placed for monitoring intracranial pressure (ICP). Repeated imaging (computed tomography [CT] or cMRI) is frequently required to guide management. A low-field (0.064-T) portable magnetic resonance imaging (pMRI) machine may provide images in situations that were previously considered contraindications for cMRI. OBSERVATIONS A 10-year-old boy with severe traumatic brain injury was admitted to the pediatric intensive care unit, and an ICB was placed. Initial head CT showed a left-sided intraparenchymal hemorrhage with intraventricular dissection and cerebral edema with mass effect. Repeated imaging was required to assess the brain structure because of continually fluctuating ICP. Transferring the patient to the radiology suite was risky because of his critical condition and the presence of an ICB; hence, pMRI was performed at the bedside. Images obtained were of excellent quality without any ICB artifact, guiding the decision to continue to manage the patient conservatively. The child later improved and was discharged from the hospital. LESSONS pMRI can be used to obtain excellent images at the bedside in patients with an ICB, providing useful information for better management of patients with neurological injuries.
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Rizvi QM, Singh S. Assessment of the low-field magnetic resonance imaging for the brain scan imaging of the infant hydrocephalus. Int J Health Sci (Qassim) 2023; 17:44-53. [PMID: 37416843 PMCID: PMC10321462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/08/2023] Open
Abstract
Objective In today's global clinical settings, low-field magnetic resonance imaging (MRI) technology is becoming increasingly prevalent. Ensuring high-quality image acquisition is crucial for accurate disease diagnosis and treatment and for evaluating the impact of poor-quality images. In this study, we explored the potential of deep learning as a diagnostic tool for improving image quality in hydrocephalus analysis planning. This could include discussions on the diagnostic accuracy, cost-effectiveness, and practicality of using low-field MRI as an alternative. Methods There are many reasons which are going to affect infant computed tomography images. These are spatial resolution, noise, and contrast between the brain and cerebrospinal fluid (CSF). Now, we can enhance using the application of deep learning algorithms. Both improved and down quality were situated to the three qualified pediatric neurosurgeons comfortable with working in poor- to middle-level income countries for the analysis of clinical tools in the planning of the treatment of hydrocephalus. Results The results predict that a picture will be classified as beneficial for hydrocephalus treatment planning, according to image resolution and the contrast-to-noise ratio (CNR) between the brain and CSF. The CNR is significantly improved by deep learning enhancement, which also improves the apparent likelihood of the image. Conclusion However, poor-quality images might be desirable to image improved by deep learning, since those images will not offer the risk of confusing facts which could misguide the decision of the analysis of patients. Such findings support the newly introduced measurement standards in estimating the acceptable quality of images for clinical use.
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Affiliation(s)
- Qaim Mehdi Rizvi
- Department of Computer Applications, Shri Ramswaroop Memorial College of Engineering and Management, Lucknow, Uttar Pradesh, India
| | - Sarika Singh
- Department of Computer Applications, Shri Ramswaroop Memorial College of Engineering and Management, Lucknow, Uttar Pradesh, India
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Altaf A, Baqai MWS, Urooj F, Alam MS, Aziz HF, Mubarak F, Knopp E, Siddiqui K, Enam SA. Intraoperative use of ultra-low-field, portable magnetic resonance imaging - first report. Surg Neurol Int 2023; 14:212. [PMID: 37404510 PMCID: PMC10316138 DOI: 10.25259/sni_124_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: 07/06/2023] Open
Abstract
Background Intraoperative use of portable magnetic resonance imaging (pMRI) has become a valuable tool in a surgeon's arsenal since its inception. It allows intraoperative localization of tumor extent and identification of residual disease, hence maximizing tumor resection. Its utility has been widespread in high-income countries for the past 20 years, but in lower-middle-income countries (LMIC), it is still not widely available due to several reasons, including cost constraints. The use of intraoperative pMRI may be a cost-effective and efficient substitute for conventional MRI machines. The authors present a case where a pMRI device was used intraoperatively in an LMIC setting. Case Description The authors performed a microscopic transsphenoidal resection of a sellar lesion with intraoperative imaging using the pMRI system on a 45-year-old man with a nonfunctioning pituitary macroadenoma. Without the need for an MRI suite or other MRI-compatible equipment, the scan was conducted within the confinements of a standard operating room. Low-field MRI showed some residual disease and postsurgical changes, comparable to postoperative high-field MRI. Conclusion To the best of our knowledge, our report provides the first documented successful intraoperative transsphenoidal resection of a pituitary adenoma using an ultra-low-field pMRI device. The device can potentially enhance neurosurgical capacity in resource-constrained settings and improve patient outcomes in developing country.
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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, Aga Khan University Hospital, Karachi, Sindh, Pakistan
| | - Edmond Knopp
- Department of Radiology, Hyperfine, Guilford, Connecticut, United States
| | - Khan 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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Selvaganesan K, Wan Y, Ha Y, Wu B, Hancock K, Galiana G, Constable RT. Magnetic resonance imaging using a nonuniform Bo (NuBo) field-cycling magnet. PLoS One 2023; 18:e0287344. [PMID: 37319289 PMCID: PMC10270621 DOI: 10.1371/journal.pone.0287344] [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: 01/16/2023] [Accepted: 06/02/2023] [Indexed: 06/17/2023] Open
Abstract
Magnetic resonance imaging (MRI) is a powerful noninvasive diagnostic tool with superior soft tissue contrast. However, access to MRI is limited since current systems depend on homogeneous, high field strength main magnets (B0-fields), with strong switchable gradients which are expensive to install and maintain. In this work we propose a new approach to MRI where imaging is performed in an inhomogeneous field using radiofrequency spatial encoding, thereby eliminating the need for uniform B0-fields and conventional cylindrical gradient coils. The proposed technology uses an innovative data acquisition and reconstruction approach by integrating developments in field cycling, parallel imaging and non-Fourier based algebraic reconstruction. The scanner uses field cycling to image in an inhomogeneous B0-field; in this way magnetization is maximized during the high field polarization phase, and B0 inhomogeneity effects are minimized by using a low field during image acquisition. In addition to presenting the concept, this work provides experimental verification of a long-lived spin echo signal, spatially varying resolution, as well as both simulated and experimental 2D images. Our initial design creates an open MR system that can be installed in a patient examination table for body imaging (e.g., breast or liver) or built into a wall for weighted-spine imaging. The proposed system introduces a new class of inexpensive, open, silent MRIs that could be housed in doctor's offices much like ultrasound is today, making MRI more widely accessible.
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Affiliation(s)
- Kartiga Selvaganesan
- Department of Biomedical Engineering, Yale University, New Haven, CT, United States of America
| | - Yuqing Wan
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States of America
| | - Yonghyun Ha
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States of America
| | - Baosong Wu
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States of America
| | - Kasey Hancock
- Department of Electrical Engineering, Yale University, New Haven, CT, United States of America
| | - Gigi Galiana
- Department of Biomedical Engineering, Yale University, New Haven, CT, United States of America
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States of America
| | - R. Todd Constable
- Department of Biomedical Engineering, Yale University, New Haven, CT, United States of America
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States of America
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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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Khanduja S, Kim J, Kang JK, Feng CY, Vogelsong MA, Geocadin RG, Whitman G, Cho SM. Hypoxic-Ischemic Brain Injury in ECMO: Pathophysiology, Neuromonitoring, and Therapeutic Opportunities. Cells 2023; 12:1546. [PMID: 37296666 PMCID: PMC10252448 DOI: 10.3390/cells12111546] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 06/02/2023] [Accepted: 06/03/2023] [Indexed: 06/12/2023] Open
Abstract
Extracorporeal membrane oxygenation (ECMO), in conjunction with its life-saving benefits, carries a significant risk of acute brain injury (ABI). Hypoxic-ischemic brain injury (HIBI) is one of the most common types of ABI in ECMO patients. Various risk factors, such as history of hypertension, high day 1 lactate level, low pH, cannulation technique, large peri-cannulation PaCO2 drop (∆PaCO2), and early low pulse pressure, have been associated with the development of HIBI in ECMO patients. The pathogenic mechanisms of HIBI in ECMO are complex and multifactorial, attributing to the underlying pathology requiring initiation of ECMO and the risk of HIBI associated with ECMO itself. HIBI is likely to occur in the peri-cannulation or peri-decannulation time secondary to underlying refractory cardiopulmonary failure before or after ECMO. Current therapeutics target pathological mechanisms, cerebral hypoxia and ischemia, by employing targeted temperature management in the case of extracorporeal cardiopulmonary resuscitation (eCPR), and optimizing cerebral O2 saturations and cerebral perfusion. This review describes the pathophysiology, neuromonitoring, and therapeutic techniques to improve neurological outcomes in ECMO patients in order to prevent and minimize the morbidity of HIBI. Further studies aimed at standardizing the most relevant neuromonitoring techniques, optimizing cerebral perfusion, and minimizing the severity of HIBI once it occurs will improve long-term neurological outcomes in ECMO patients.
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Affiliation(s)
- Shivalika Khanduja
- Division of Cardiac Surgery, Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; (S.K.); (J.K.K.); (G.W.)
| | - Jiah Kim
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; (J.K.); (C.-Y.F.)
| | - Jin Kook Kang
- Division of Cardiac Surgery, Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; (S.K.); (J.K.K.); (G.W.)
| | - Cheng-Yuan Feng
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; (J.K.); (C.-Y.F.)
| | - Melissa Ann Vogelsong
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA;
| | - Romergryko G. Geocadin
- Divisions of Neurosciences Critical Care, Departments of Neurology, Surgery, Anesthesiology and Critical Care Medicine and Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA;
| | - Glenn Whitman
- Division of Cardiac Surgery, Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; (S.K.); (J.K.K.); (G.W.)
| | - Sung-Min Cho
- Division of Cardiac Surgery, Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; (S.K.); (J.K.K.); (G.W.)
- Divisions of Neurosciences Critical Care, Departments of Neurology, Surgery, Anesthesiology and Critical Care Medicine and Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA;
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Morris NA, Sarwal A. Neurologic Complications of Critical Medical Illness. Continuum (Minneap Minn) 2023; 29:848-886. [PMID: 37341333 DOI: 10.1212/con.0000000000001278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/22/2023]
Abstract
OBJECTIVE This article reviews the neurologic complications encountered in patients admitted to non-neurologic intensive care units, outlines various scenarios in which a neurologic consultation can add to the diagnosis or management of a critically ill patient, and provides advice on the best diagnostic approach in the evaluation of these patients. LATEST DEVELOPMENTS Increasing recognition of neurologic complications and their adverse impact on long-term outcomes has led to increased neurology involvement in non-neurologic intensive care units. The COVID-19 pandemic has highlighted the importance of having a structured clinical approach to neurologic complications of critical illness as well as the critical care management of patients with chronic neurologic disabilities. ESSENTIAL POINTS Critical illness is often accompanied by neurologic complications. Neurologists need to be aware of the unique needs of critically ill patients, especially the nuances of the neurologic examination, challenges in diagnostic testing, and neuropharmacologic aspects of commonly used medications.
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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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de Vos B, Remis RF, Webb AG. An integrated target field framework for point-of-care halbach array low-field MRI system design. MAGMA (NEW YORK, N.Y.) 2023:10.1007/s10334-023-01093-z. [PMID: 37208554 PMCID: PMC10386967 DOI: 10.1007/s10334-023-01093-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 01/18/2023] [Accepted: 04/16/2023] [Indexed: 05/21/2023]
Abstract
OBJECTIVE Low-cost low-field point-of-care MRI systems are used in many different applications. System design has correspondingly different requirements in terms of imaging field-of-view, spatial resolution and magnetic field strength. In this work an iterative framework has been created to design a cylindrical Halbach-based magnet along with integrated gradient and RF coils that most efficiently fulfil a set of user-specified imaging requirements. METHODS For efficient integration, target field methods are used for each of the main hardware components. These have not been used previously in magnet design, and a new mathematical model was derived accordingly. These methods result in a framework which can design an entire low-field MRI system within minutes using standard computing hardware. RESULTS Two distinct point-of-care systems are designed using the described framework, one for neuroimaging and the other for extremity imaging. Input parameters are taken from literature and the resulting systems are discussed in detail. DISCUSSION The framework allows the designer to optimize the different hardware components with respect to the desired imaging parameters taking into account the interdependencies between these components and thus give insight into the influence of the design choices.
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Affiliation(s)
- Bart de Vos
- C.J. Gorter MRI Center, Leiden University Medical Center, Leiden, Netherlands.
| | - Rob F Remis
- Signal Processing Systems, Delft University of Technology, Delft, Netherlands
| | - Andrew G Webb
- C.J. Gorter MRI Center, Leiden University Medical Center, Leiden, Netherlands
- Signal Processing Systems, Delft University of Technology, Delft, Netherlands
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Martin MN, Jordanova KV, Kos AB, Russek SE, Keenan KE, Stupic KF. Relaxation measurements of an MRI system phantom at low magnetic field strengths. MAGMA (NEW YORK, N.Y.) 2023:10.1007/s10334-023-01086-y. [PMID: 37209233 PMCID: PMC10386925 DOI: 10.1007/s10334-023-01086-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 02/23/2023] [Accepted: 03/29/2023] [Indexed: 05/22/2023]
Abstract
OBJECTIVE Temperature controlled T1 and T2 relaxation times are measured on NiCl2 and MnCl2 solutions from the ISMRM/NIST system phantom at low magnetic field strengths of 6.5 mT, 64 mT and 550 mT. MATERIALS AND METHODS The T1 and T2 were measured of five samples with increasing concentrations of NiCl2 and five samples with increasing concentrations of MnCl2. All samples were scanned at 6.5 mT, 64 mT and 550 mT, at sample temperatures ranging from 10 °C to 37 °C. RESULTS The NiCl2 solutions showed little change in T1 and T2 with magnetic field strength, and both relaxation times decreased with increasing temperature. The MnCl2 solutions showed an increase in T1 and a decrease in T2 with increasing magnetic field strength, and both T1 and T2 increased with increasing temperature. DISCUSSION The low field relaxation rates of the NiCl2 and MnCl2 arrays in the ISMRM/NIST system phantom are investigated and compared to results from clinical field strengths of 1.5 T and 3.0 T. The measurements can be used as a benchmark for MRI system functionality and stability, especially when MRI systems are taken out of the radiology suite or laboratory and into less traditional environments.
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Affiliation(s)
- Michele N Martin
- U.S. Department of Commerce, National Institute of Standards and Technology, 325 Broadway, Boulder, CO, 80305, USA.
| | - Kalina V Jordanova
- U.S. Department of Commerce, National Institute of Standards and Technology, 325 Broadway, Boulder, CO, 80305, USA
| | - Anthony B Kos
- U.S. Department of Commerce, National Institute of Standards and Technology, 325 Broadway, Boulder, CO, 80305, USA
| | - Stephen E Russek
- U.S. Department of Commerce, National Institute of Standards and Technology, 325 Broadway, Boulder, CO, 80305, USA
| | - Kathryn E Keenan
- U.S. Department of Commerce, National Institute of Standards and Technology, 325 Broadway, Boulder, CO, 80305, USA
| | - Karl F Stupic
- U.S. Department of Commerce, National Institute of Standards and Technology, 325 Broadway, Boulder, CO, 80305, USA
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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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Haskell MW, Nielsen JF, Noll DC. Off-resonance artifact correction for MRI: A review. NMR IN BIOMEDICINE 2023; 36:e4867. [PMID: 36326709 PMCID: PMC10284460 DOI: 10.1002/nbm.4867] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 09/25/2022] [Accepted: 11/01/2022] [Indexed: 06/06/2023]
Abstract
In magnetic resonance imaging (MRI), inhomogeneity in the main magnetic field used for imaging, referred to as off-resonance, can lead to image artifacts ranging from mild to severe depending on the application. Off-resonance artifacts, such as signal loss, geometric distortions, and blurring, can compromise the clinical and scientific utility of MR images. In this review, we describe sources of off-resonance in MRI, how off-resonance affects images, and strategies to prevent and correct for off-resonance. Given recent advances and the great potential of low-field and/or portable MRI, we also highlight the advantages and challenges of imaging at low field with respect to off-resonance.
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Affiliation(s)
- Melissa W Haskell
- Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan, USA
- Hyperfine Research, Guilford, Connecticut, USA
| | | | - Douglas C Noll
- Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA
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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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Parsa J, Webb A. Specific absorption rate (SAR) simulations for low-field (< 0.1 T) MRI systems. MAGMA (NEW YORK, N.Y.) 2023:10.1007/s10334-023-01073-3. [PMID: 36933091 PMCID: PMC10386976 DOI: 10.1007/s10334-023-01073-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 01/19/2023] [Accepted: 02/23/2023] [Indexed: 03/19/2023]
Abstract
OBJECTIVE To simulate the magnetic and electric fields produced by RF coil geometries commonly used at low field. Based on these simulations, the specific absorption rate (SAR) efficiency can be derived to ensure safe operation even when using short RF pulses and high duty cycles. METHODS Electromagnetic simulations were performed at four different field strengths between 0.05 and 0.1 T, corresponding to the lower and upper limits of current point-of-care (POC) neuroimaging systems. Transmit magnetic and electric fields, as well as transmit efficiency and SAR efficiency were simulated. The effects of a close-fitting shield on the EM fields were also assessed. SAR calculations were performed as a function of RF pulse length in turbo-spin echo (TSE) sequences. RESULTS Simulations of RF coil characteristics and B1+ transmit efficiencies agreed well with corresponding experimentally determined parameters. Overall, the SAR efficiency was, as expected, higher at the lower frequencies studied, and many orders of magnitude greater than at conventional clinical field strengths. The tight-fitting transmit coil results in the highest SAR in the nose and skull, which are not thermally sensitive tissues. The calculated SAR efficiencies showed that only when 180° refocusing pulses of duration ~ 10 ms are used for TSE sequences does SAR need to be carefully considered. CONCLUSION This work presents a comprehensive overview of the transmit and SAR efficiencies for RF coils used for POC MRI neuroimaging. While SAR is not a problem for conventional sequences, the values derived here should be useful for RF intensive sequences such as T1ρ, and also demonstrate that if very short RF pulses are required then SAR calculations should be performed.
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Affiliation(s)
- Javad Parsa
- C.J. Gorter MRI Centre, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Percuros B.V., Leiden, The Netherlands
| | - Andrew Webb
- C.J. Gorter MRI Centre, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
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Lynch DG, Narayan RK, Li C. Multi-Mechanistic Approaches to the Treatment of Traumatic Brain Injury: A Review. J Clin Med 2023; 12:jcm12062179. [PMID: 36983181 PMCID: PMC10052098 DOI: 10.3390/jcm12062179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 03/08/2023] [Accepted: 03/09/2023] [Indexed: 03/18/2023] Open
Abstract
Traumatic brain injury (TBI) is a leading cause of death and disability worldwide. Despite extensive research efforts, the majority of trialed monotherapies to date have failed to demonstrate significant benefit. It has been suggested that this is due to the complex pathophysiology of TBI, which may possibly be addressed by a combination of therapeutic interventions. In this article, we have reviewed combinations of different pharmacologic treatments, combinations of non-pharmacologic interventions, and combined pharmacologic and non-pharmacologic interventions for TBI. Both preclinical and clinical studies have been included. While promising results have been found in animal models, clinical trials of combination therapies have not yet shown clear benefit. This may possibly be due to their application without consideration of the evolving pathophysiology of TBI. Improvements of this paradigm may come from novel interventions guided by multimodal neuromonitoring and multimodal imaging techniques, as well as the application of multi-targeted non-pharmacologic and endogenous therapies. There also needs to be a greater representation of female subjects in preclinical and clinical studies.
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Affiliation(s)
- Daniel G. Lynch
- Translational Brain Research Laboratory, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
- Zucker School of Medicine at Hofstra/Northwell Health, Hempstead, NY 11549, USA
| | - Raj K. Narayan
- Translational Brain Research Laboratory, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
- Department of Neurosurgery, St. Francis Hospital, Roslyn, NY 11576, USA
| | - Chunyan Li
- Translational Brain Research Laboratory, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
- Zucker School of Medicine at Hofstra/Northwell Health, Hempstead, NY 11549, USA
- Department of Neurosurgery, Northwell Health, Manhasset, NY 11030, USA
- Correspondence:
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50
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Iglesias JE, Schleicher R, Laguna S, Billot B, Schaefer P, McKaig B, Goldstein JN, Sheth KN, Rosen MS, Kimberly WT. Quantitative Brain Morphometry of Portable Low-Field-Strength MRI Using Super-Resolution Machine Learning. Radiology 2023; 306:e220522. [PMID: 36346311 PMCID: PMC9968773 DOI: 10.1148/radiol.220522] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 08/29/2022] [Accepted: 09/08/2022] [Indexed: 11/09/2022]
Abstract
Background Portable, low-field-strength (0.064-T) MRI has the potential to transform neuroimaging but is limited by low spatial resolution and low signal-to-noise ratio. Purpose To implement a machine learning super-resolution algorithm that synthesizes higher spatial resolution images (1-mm isotropic) from lower resolution T1-weighted and T2-weighted portable brain MRI scans, making them amenable to automated quantitative morphometry. Materials and Methods An external high-field-strength MRI data set (1-mm isotropic scans from the Open Access Series of Imaging Studies data set) and segmentations for 39 regions of interest (ROIs) in the brain were used to train a super-resolution convolutional neural network (CNN). Secondary analysis of an internal test set of 24 paired low- and high-field-strength clinical MRI scans in participants with neurologic symptoms was performed. These were part of a prospective observational study (August 2020 to December 2021) at Massachusetts General Hospital (exclusion criteria: inability to lay flat, body habitus preventing low-field-strength MRI, presence of MRI contraindications). Three well-established automated segmentation tools were applied to three sets of scans: high-field-strength (1.5-3 T, reference standard), low-field-strength (0.064 T), and synthetic high-field-strength images generated from the low-field-strength data with the CNN. Statistical significance of correlations was assessed with Student t tests. Correlation coefficients were compared with Steiger Z tests. Results Eleven participants (mean age, 50 years ± 14; seven men) had full cerebrum coverage in the images without motion artifacts or large stroke lesion with distortion from mass effect. Direct segmentation of low-field-strength MRI yielded nonsignificant correlations with volumetric measurements from high field strength for most ROIs (P > .05). Correlations largely improved when segmenting the synthetic images: P values were less than .05 for all ROIs (eg, for the hippocampus [r = 0.85; P < .001], thalamus [r = 0.84; P = .001], and whole cerebrum [r = 0.92; P < .001]). Deviations from the model (z score maps) visually correlated with pathologic abnormalities. Conclusion This work demonstrated proof-of-principle augmentation of portable MRI with a machine learning super-resolution algorithm, which yielded highly correlated brain morphometric measurements to real higher resolution images. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Ertl-Wagner amd Wagner in this issue. An earlier incorrect version appeared online. This article was corrected on February 1, 2023.
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Affiliation(s)
- Juan Eugenio Iglesias
- From the Athinoula A. Martinos Center for Biomedical Imaging (J.E.I.,
M.S.R.), Department of Radiology (J.E.I., P.S., M.S.R.), Department of Neurology
and Center for Genomic Medicine (R.S., W.T.K.), and Department of Emergency
Medicine (B.M., J.N.G.), Massachusetts General Hospital and Harvard Medical
School, 55 Fruit St, Boston, MA 02114; Centre for Medical Image Computing,
Department of Medical Physics and Biomedical Engineering, University College
London, London, UK (J.E.I., B.B.); Computer Science and Artificial Intelligence
Laboratory, Massachusetts Institute of Technology, Cambridge, Mass (J.E.I.);
Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (S.L.);
Department of Neurology, Yale New Haven Hospital, New Haven, Conn (K.N.S.); and
Department of Physics, Harvard University, Cambridge, Mass (M.S.R.)
| | - Riana Schleicher
- From the Athinoula A. Martinos Center for Biomedical Imaging (J.E.I.,
M.S.R.), Department of Radiology (J.E.I., P.S., M.S.R.), Department of Neurology
and Center for Genomic Medicine (R.S., W.T.K.), and Department of Emergency
Medicine (B.M., J.N.G.), Massachusetts General Hospital and Harvard Medical
School, 55 Fruit St, Boston, MA 02114; Centre for Medical Image Computing,
Department of Medical Physics and Biomedical Engineering, University College
London, London, UK (J.E.I., B.B.); Computer Science and Artificial Intelligence
Laboratory, Massachusetts Institute of Technology, Cambridge, Mass (J.E.I.);
Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (S.L.);
Department of Neurology, Yale New Haven Hospital, New Haven, Conn (K.N.S.); and
Department of Physics, Harvard University, Cambridge, Mass (M.S.R.)
| | - Sonia Laguna
- From the Athinoula A. Martinos Center for Biomedical Imaging (J.E.I.,
M.S.R.), Department of Radiology (J.E.I., P.S., M.S.R.), Department of Neurology
and Center for Genomic Medicine (R.S., W.T.K.), and Department of Emergency
Medicine (B.M., J.N.G.), Massachusetts General Hospital and Harvard Medical
School, 55 Fruit St, Boston, MA 02114; Centre for Medical Image Computing,
Department of Medical Physics and Biomedical Engineering, University College
London, London, UK (J.E.I., B.B.); Computer Science and Artificial Intelligence
Laboratory, Massachusetts Institute of Technology, Cambridge, Mass (J.E.I.);
Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (S.L.);
Department of Neurology, Yale New Haven Hospital, New Haven, Conn (K.N.S.); and
Department of Physics, Harvard University, Cambridge, Mass (M.S.R.)
| | - Benjamin Billot
- From the Athinoula A. Martinos Center for Biomedical Imaging (J.E.I.,
M.S.R.), Department of Radiology (J.E.I., P.S., M.S.R.), Department of Neurology
and Center for Genomic Medicine (R.S., W.T.K.), and Department of Emergency
Medicine (B.M., J.N.G.), Massachusetts General Hospital and Harvard Medical
School, 55 Fruit St, Boston, MA 02114; Centre for Medical Image Computing,
Department of Medical Physics and Biomedical Engineering, University College
London, London, UK (J.E.I., B.B.); Computer Science and Artificial Intelligence
Laboratory, Massachusetts Institute of Technology, Cambridge, Mass (J.E.I.);
Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (S.L.);
Department of Neurology, Yale New Haven Hospital, New Haven, Conn (K.N.S.); and
Department of Physics, Harvard University, Cambridge, Mass (M.S.R.)
| | - Pamela Schaefer
- From the Athinoula A. Martinos Center for Biomedical Imaging (J.E.I.,
M.S.R.), Department of Radiology (J.E.I., P.S., M.S.R.), Department of Neurology
and Center for Genomic Medicine (R.S., W.T.K.), and Department of Emergency
Medicine (B.M., J.N.G.), Massachusetts General Hospital and Harvard Medical
School, 55 Fruit St, Boston, MA 02114; Centre for Medical Image Computing,
Department of Medical Physics and Biomedical Engineering, University College
London, London, UK (J.E.I., B.B.); Computer Science and Artificial Intelligence
Laboratory, Massachusetts Institute of Technology, Cambridge, Mass (J.E.I.);
Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (S.L.);
Department of Neurology, Yale New Haven Hospital, New Haven, Conn (K.N.S.); and
Department of Physics, Harvard University, Cambridge, Mass (M.S.R.)
| | - Brenna McKaig
- From the Athinoula A. Martinos Center for Biomedical Imaging (J.E.I.,
M.S.R.), Department of Radiology (J.E.I., P.S., M.S.R.), Department of Neurology
and Center for Genomic Medicine (R.S., W.T.K.), and Department of Emergency
Medicine (B.M., J.N.G.), Massachusetts General Hospital and Harvard Medical
School, 55 Fruit St, Boston, MA 02114; Centre for Medical Image Computing,
Department of Medical Physics and Biomedical Engineering, University College
London, London, UK (J.E.I., B.B.); Computer Science and Artificial Intelligence
Laboratory, Massachusetts Institute of Technology, Cambridge, Mass (J.E.I.);
Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (S.L.);
Department of Neurology, Yale New Haven Hospital, New Haven, Conn (K.N.S.); and
Department of Physics, Harvard University, Cambridge, Mass (M.S.R.)
| | - Joshua N. Goldstein
- From the Athinoula A. Martinos Center for Biomedical Imaging (J.E.I.,
M.S.R.), Department of Radiology (J.E.I., P.S., M.S.R.), Department of Neurology
and Center for Genomic Medicine (R.S., W.T.K.), and Department of Emergency
Medicine (B.M., J.N.G.), Massachusetts General Hospital and Harvard Medical
School, 55 Fruit St, Boston, MA 02114; Centre for Medical Image Computing,
Department of Medical Physics and Biomedical Engineering, University College
London, London, UK (J.E.I., B.B.); Computer Science and Artificial Intelligence
Laboratory, Massachusetts Institute of Technology, Cambridge, Mass (J.E.I.);
Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (S.L.);
Department of Neurology, Yale New Haven Hospital, New Haven, Conn (K.N.S.); and
Department of Physics, Harvard University, Cambridge, Mass (M.S.R.)
| | - Kevin N. Sheth
- From the Athinoula A. Martinos Center for Biomedical Imaging (J.E.I.,
M.S.R.), Department of Radiology (J.E.I., P.S., M.S.R.), Department of Neurology
and Center for Genomic Medicine (R.S., W.T.K.), and Department of Emergency
Medicine (B.M., J.N.G.), Massachusetts General Hospital and Harvard Medical
School, 55 Fruit St, Boston, MA 02114; Centre for Medical Image Computing,
Department of Medical Physics and Biomedical Engineering, University College
London, London, UK (J.E.I., B.B.); Computer Science and Artificial Intelligence
Laboratory, Massachusetts Institute of Technology, Cambridge, Mass (J.E.I.);
Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (S.L.);
Department of Neurology, Yale New Haven Hospital, New Haven, Conn (K.N.S.); and
Department of Physics, Harvard University, Cambridge, Mass (M.S.R.)
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