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Arnold TC, Freeman CW, Litt B, Stein JM. Low-field MRI: Clinical promise and challenges. J Magn Reson Imaging 2023; 57:25-44. [PMID: 36120962 PMCID: PMC9771987 DOI: 10.1002/jmri.28408] [Citation(s) in RCA: 50] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 08/11/2022] [Accepted: 08/12/2022] [Indexed: 02/03/2023] Open
Abstract
Modern MRI scanners have trended toward higher field strengths to maximize signal and resolution while minimizing scan time. However, high-field devices remain expensive to install and operate, making them scarce outside of high-income countries and major population centers. Low-field strength scanners have drawn renewed academic, industry, and philanthropic interest due to advantages that could dramatically increase imaging access, including lower cost and portability. Nevertheless, low-field MRI still faces inherent limitations in image quality that come with decreased signal. In this article, we review advantages and disadvantages of low-field MRI scanners, describe hardware and software innovations that accentuate advantages and mitigate disadvantages, and consider clinical applications for a new generation of low-field devices. In our review, we explore how these devices are being or could be used for high acuity brain imaging, outpatient neuroimaging, MRI-guided procedures, pediatric imaging, and musculoskeletal imaging. Challenges for their successful clinical translation include selecting and validating appropriate use cases, integrating with standards of care in high resource settings, expanding options with actionable information in low resource settings, and facilitating health care providers and clinical practice in new ways. By embracing both the promise and challenges of low-field MRI, clinicians and researchers have an opportunity to transform medical care for patients around the world. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.
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Affiliation(s)
- Thomas Campbell Arnold
- Department of Bioengineering, School of Engineering & Applied ScienceUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Neuroengineering and TherapeuticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Colbey W. Freeman
- Department of Radiology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Brian Litt
- Center for Neuroengineering and TherapeuticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Neurology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Joel M. Stein
- Center for Neuroengineering and TherapeuticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Radiology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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The Application of Convolutional Neural Network Model in Diagnosis and Nursing of MR Imaging in Alzheimer's Disease. Interdiscip Sci 2021; 14:34-44. [PMID: 34224083 DOI: 10.1007/s12539-021-00450-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 05/07/2021] [Accepted: 06/07/2021] [Indexed: 10/20/2022]
Abstract
The disease Alzheimer is an irrepressible neurologicalbrain disorder. Earlier detection and proper treatment of Alzheimer's disease can help for brain tissue damage prevention. The study was intended to explore the segmentation effects of convolutional neural network (CNN) model on Magnetic Resonance (MR) imaging for Alzheimer's diagnosis and nursing. Specifically, 18 Alzheimer's patients admitted to Indira Gandhi Medical College (IGMC) hospital were selected as the experimental group, with 18 healthy volunteers in the Ctrl group. Furthermore, the CNN model was applied to segment the MR imaging of Alzheimer's patients, and its segmentation effects were compared with those of the fully convolutional neural network (FCNN) and support vector machine (SVM) algorithms. It was found that the CNN model demonstrated higher segmentation precision, and the experimental group showed a higher clinical dementia rating (CDR) score and a lower mini-mental state examination (MMSE) score (P < 0.05). The size of parahippocompalgyrus and putamen was bigger in the Ctrl (P < 0.05). In experimental group, the amplitude of low-frequency fluctuation (ALFF) was positively correlated with the MMSE score in areas of bilateral cingulum gyri (r = 0.65) and precuneus (r = 0.59). In conclusion, the grey matter structure is damaged in Alzheimer's patients, and hippocampus ALFF and regional homogeneity (ReHo) is involved in the neuronal compensation mechanism of hippocampal damage, and the caregivers should take an active nursing method.
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Rogers CM, Jones PS, Weinberg JS. Intraoperative MRI for Brain Tumors. J Neurooncol 2021; 151:479-490. [PMID: 33611714 DOI: 10.1007/s11060-020-03667-6] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 11/23/2020] [Indexed: 02/07/2023]
Abstract
INTRODUCTION The use of intraoperative imaging has been a critical tool in the neurosurgeon's armamentarium and is of particular benefit during tumor surgery. This article summarizes the history of its development, implementation, clinical experience and future directions. METHODS We reviewed the literature focusing on the development and clinical experience with intraoperative MRI. Utilizing the authors' personal experience as well as evidence from the literature, we present an overview of the utility of MRI during neurosurgery. RESULTS In the 1990s, the first description of using a low field MRI in the operating room was published describing the additional benefit provided by improved resolution of MRI as compared to ultrasound. Since then, implementation has varied in magnetic field strength and in configuration from floor mounted to ceiling mounted units as well as those that are accessible to the operating room for use during surgery and via an outpatient entrance to use for diagnostic imaging. The experience shows utility of this technique for increasing extent of resection for low and high grade tumors as well as preventing injury to important structures while incorporating techniques such as intraoperative monitoring. CONCLUSION This article reviews the history of intraoperative MRI and presents a review of the literature revealing the successful implementation of this technology and benefits noted for the patient and the surgeon.
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Affiliation(s)
- Cara Marie Rogers
- Department of Neurosurgery, Virginia Tech Carilion, Roanoke, VA, USA
| | - Pamela S Jones
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jeffrey S Weinberg
- Department of Neurosurgery, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA.
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Zaffino P, Moccia S, De Momi E, Spadea MF. A Review on Advances in Intra-operative Imaging for Surgery and Therapy: Imagining the Operating Room of the Future. Ann Biomed Eng 2020; 48:2171-2191. [PMID: 32601951 DOI: 10.1007/s10439-020-02553-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Accepted: 06/17/2020] [Indexed: 12/19/2022]
Abstract
With the advent of Minimally Invasive Surgery (MIS), intra-operative imaging has become crucial for surgery and therapy guidance, allowing to partially compensate for the lack of information typical of MIS. This paper reviews the advancements in both classical (i.e. ultrasounds, X-ray, optical coherence tomography and magnetic resonance imaging) and more recent (i.e. multispectral, photoacoustic and Raman imaging) intra-operative imaging modalities. Each imaging modality was analyzed, focusing on benefits and disadvantages in terms of compatibility with the operating room, costs, acquisition time and image characteristics. Tables are included to summarize this information. New generation of hybrid surgical room and algorithms for real time/in room image processing were also investigated. Each imaging modality has its own (site- and procedure-specific) peculiarities in terms of spatial and temporal resolution, field of view and contrasted tissues. Besides the benefits that each technique offers for guidance, considerations about operators and patient risk, costs, and extra time required for surgical procedures have to be considered. The current trend is to equip surgical rooms with multimodal imaging systems, so as to integrate multiple information for real-time data extraction and computer-assisted processing. The future of surgery is to enhance surgeons eye to minimize intra- and after-surgery adverse events and provide surgeons with all possible support to objectify and optimize the care-delivery process.
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Affiliation(s)
- Paolo Zaffino
- Department of Experimental and Clinical Medicine, Universitá della Magna Graecia, Catanzaro, Italy
| | - Sara Moccia
- Department of Information Engineering (DII), Universitá Politecnica delle Marche, via Brecce Bianche, 12, 60131, Ancona, AN, Italy.
| | - Elena De Momi
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133, Milano, MI, Italy
| | - Maria Francesca Spadea
- Department of Experimental and Clinical Medicine, Universitá della Magna Graecia, Catanzaro, Italy
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Dimaras H, Corson TW. Retinoblastoma, the visible CNS tumor: A review. J Neurosci Res 2019; 97:29-44. [PMID: 29314142 PMCID: PMC6034991 DOI: 10.1002/jnr.24213] [Citation(s) in RCA: 104] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Revised: 12/02/2017] [Accepted: 12/11/2017] [Indexed: 12/11/2022]
Abstract
The pediatric ocular cancer retinoblastoma is the only central nervous system (CNS) tumor readily observed without specialized equipment: it can be seen by, and in, the naked eye. This accessibility enables unique imaging modalities. Here, we review this cancer for a neuroscience audience, highlighting these clinical and research imaging options, including fundus imaging, optical coherence tomography, ultrasound, and magnetic resonance imaging. We also discuss the subtype of retinoblastoma driven by the MYCN oncogene more commonly associated with neuroblastoma, and consider trilateral retinoblastoma, in which an intracranial tumor arises along with ocular tumors in patients with germline RB1 gene mutations. Retinoblastoma research and clinical care can offer insights applicable to CNS malignancies, and also benefit from approaches developed elsewhere in the CNS.
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Affiliation(s)
- Helen Dimaras
- Department of Ophthalmology and Vision Sciences, Faculty of Medicine, University of Toronto, Toronto, ON, M5S 1A8, Canada
- Division of Clinical Public Health, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, M5S 1A8, Canada
- Department of Ophthalmology and Vision Sciences, The Hospital for Sick Children, Toronto, ON, M5G 1X8, Canada
- Child Health Evaluative Sciences Program, SickKids Research Institute, Toronto, ON, M5G 1X8, Canada
- Department of Human Pathology, College of Health Sciences, University of Nairobi, Nairobi, Kenya
| | - Timothy W. Corson
- Eugene and Marilyn Glick Eye Institute, Department of Ophthalmology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Pharmacology & Toxicology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana University Melvin and Bren Simon Cancer Center, Indianapolis, IN, 46202, USA
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Hu S, Kang H, Baek Y, El Fakhri G, Kuang A, Choi HS. Real-Time Imaging of Brain Tumor for Image-Guided Surgery. Adv Healthc Mater 2018; 7:e1800066. [PMID: 29719137 PMCID: PMC6105507 DOI: 10.1002/adhm.201800066] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Revised: 03/22/2018] [Indexed: 02/05/2023]
Abstract
The completion of surgical resection is a key prognostic factor in brain tumor treatment. This requires surgeons to identify residual tumors in theater as well as to margin the proximity of the tumor to adjacent normal tissue. Subjective assessments, such as texture palpation or visual tissue differences, are commonly used by oncology surgeons during resection to differentiate cancer lesions from normal tissue, which can potentially result in either an incomplete tumor resection, or accidental removal of normal tissue. Moreover, malignant brain tumors are even more difficult to distinguish from normal brain tissue, and resecting noncancerous tissue may create neurological defects after surgery. To optimize the resection margin in brain tumors, a variety of intraoperative guidance techniques are developed, such as neuronavigation, magnetic resonance imaging, ultrasound, Raman spectroscopy, and optical fluorescence imaging. When combined with appropriate contrast agents, optical fluorescence imaging can provide the neurosurgeon real-time image guidance to improve resection completeness and to decrease surgical complications.
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Affiliation(s)
- Shuang Hu
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu, 610041, China
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Homan Kang
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Yoonji Baek
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Georges El Fakhri
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Anren Kuang
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Hak Soo Choi
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
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