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External Hardware and Sensors, for Improved MRI.
Madore, Bruno; Hess, Aaron T; van Niekerk, Adam M J; Hoinkiss, Daniel C; Hucker, Patrick; Zaitsev, Maxim; Afacan, Onur; Günther, Matthias.
Affiliation
  • Madore B; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Hess AT; Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
  • van Niekerk AMJ; Karolinska Institutet, Solna, Sweden.
  • Hoinkiss DC; Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.
  • Hucker P; Division of Medical Physics, Department of Diagnostic and Interventional Radiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  • Zaitsev M; Division of Medical Physics, Department of Diagnostic and Interventional Radiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  • Afacan O; Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Günther M; Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.
J Magn Reson Imaging ; 57(3): 690-705, 2023 03.
Article in En | MEDLINE | ID: mdl-36326548
ABSTRACT
Complex engineered systems are often equipped with suites of sensors and ancillary devices that monitor their performance and maintenance needs. MRI scanners are no different in this regard. Some of the ancillary devices available to support MRI equipment, the ones of particular interest here, have the distinction of actually participating in the image acquisition process itself. Most commonly, such devices are used to monitor physiological motion or variations in the scanner's imaging fields, allowing the imaging and/or reconstruction process to adapt as imaging conditions change. "Classic" examples include electrocardiography (ECG) leads and respiratory bellows to monitor cardiac and respiratory motion, which have been standard equipment in scan rooms since the early days of MRI. Since then, many additional sensors and devices have been proposed to support MRI acquisitions. The main physical properties that they measure may be primarily "mechanical" (eg acceleration, speed, and torque), "acoustic" (sound and ultrasound), "optical" (light and infrared), or "electromagnetic" in nature. A review of these ancillary devices, as currently available in clinical and research settings, is presented here. In our opinion, these devices are not in competition with each other as long as they provide useful and unique information, do not interfere with each other and are not prohibitively cumbersome to use, they might find their proper place in future suites of sensors. In time, MRI acquisitions will likely include a plurality of complementary signals. A little like the microbiome that provides genetic diversity to organisms, these devices can provide signal diversity to MRI acquisitions and enrich measurements. Machine-learning (ML) algorithms are well suited at combining diverse input signals toward coherent outputs, and they could make use of all such information toward improved MRI capabilities. EVIDENCE LEVEL 2 TECHNICAL EFFICACY Stage 1.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Magnetic Resonance Imaging / Heart Limits: Humans Language: En Journal: J Magn Reson Imaging Journal subject: DIAGNOSTICO POR IMAGEM Year: 2023 Document type: Article Affiliation country:

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Magnetic Resonance Imaging / Heart Limits: Humans Language: En Journal: J Magn Reson Imaging Journal subject: DIAGNOSTICO POR IMAGEM Year: 2023 Document type: Article Affiliation country:
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