External Hardware and Sensors, for ImprovedMRI
Article Swipe
Bruno Madore
,
Aaron T. Hess
,
Adam M. J. van Niekerk
,
Daniel Hoinkiss
,
Patrick Hucker
,
Maxim Zaitsev
,
Onur Afacan
,
Matthias Günther
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1002/jmri.28472
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1002/jmri.28472
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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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.1002/jmri.28472
- OA Status
- green
- Cited By
- 17
- References
- 92
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4308053981Canonical identifier for this work in OpenAlex
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https://doi.org/10.1002/jmri.28472Digital Object Identifier
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External Hardware and Sensors, for Improved
MRI Work title - Type
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reviewOpenAlex work type
- Language
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enPrimary language
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2022Year of publication
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2022-11-03Full publication date if available
- Authors
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Bruno Madore, Aaron T. Hess, Adam M. J. van Niekerk, Daniel Hoinkiss, Patrick Hucker, Maxim Zaitsev, Onur Afacan, Matthias GüntherList of authors in order
- Landing page
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https://doi.org/10.1002/jmri.28472Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://www.ncbi.nlm.nih.gov/pmc/articles/9957809Direct OA link when available
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Computer science, Process (computing), Scanner, Computer hardware, SIGNAL (programming language), Echo (communications protocol), Electronics, Artificial intelligence, Simulation, Real-time computing, Electrical engineering, Engineering, Operating system, Computer network, Programming languageTop concepts (fields/topics) attached by OpenAlex
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17Total citation count in OpenAlex
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2025: 11, 2024: 4, 2023: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.capabilities. | 279 |
| abstract_inverted_index.complementary | 228 |
| abstract_inverted_index.measurements. | 252 |
| abstract_inverted_index.participating | 49 |
| abstract_inverted_index.physiological | 64 |
| abstract_inverted_index.prohibitively | 203 |
| abstract_inverted_index.“Classic” | 85 |
| abstract_inverted_index.“optical” | 148 |
| abstract_inverted_index.reconstruction | 77 |
| abstract_inverted_index.“acoustic” | 144 |
| abstract_inverted_index.“mechanical” | 138 |
| abstract_inverted_index.Machine‐learning | 253 |
| abstract_inverted_index.electrocardiography | 88 |
| abstract_inverted_index.“electromagnetic” | 153 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5043690832 |
| countries_distinct_count | 4 |
| institutions_distinct_count | 8 |
| corresponding_institution_ids | https://openalex.org/I1283280774, https://openalex.org/I136199984 |
| citation_normalized_percentile.value | 0.89816168 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |