Evidence map›Paper›PMID 36326548›Full record

ReviewJournal of magnetic resonance imaging : JMRI2023

External Hardware and Sensors, for Improved MRI.

Bruno Madore, Aaron T Hess, Adam M J van Niekerk, Daniel C Hoinkiss, Patrick Hucker, Maxim Zaitsev, Onur Afacan, Matthias Günther

Open access · greenAbstract readReview
In one paragraph

Review in Journal of magnetic resonance imaging : JMRI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.6field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 21 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Free-breathing MRI techniques for fat and RMagma (New York, N.Y.) · 2024
    Review
  12. Article
  13. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 7 institutions in 4 countries.

Bruno MadoreDepartment of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0002-5606-8771
Aaron T HessWellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Adam M J van NiekerkKarolinska Institutet, Solna, Sweden.
Daniel C HoinkissFraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.
Patrick HuckerDivision of Medical Physics, Department of Diagnostic and Interventional Radiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Maxim ZaitsevDivision of Medical Physics, Department of Diagnostic and Interventional Radiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Onur AfacanComputational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0003-2112-3205
Matthias GüntherFraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.
University of Freiburg · DEBoston Children's Hospital · USBrigham and Women's Hospital · USFraunhofer Institute for Digital Medicine · DEKarolinska Institutet · SEUniversity of Bremen · DEUniversity of Oxford · GB

Funding

Improved Motion Robust MRI of ChildrenR01EB019483 · NIBIB · BOSTON CHILDREN'S HOSPITAL · PI WARFIELD, SIMON K · 2015 to 2024
$3.9M
Development of Methods for a Simplified and Reliable Prostate Cancer MRI ExamR01CA241817 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI MAIER, STEPHAN E · 2020 to 2025
$2.3M
Next-generation in-vivo fetal neuroimagingR01EB031849 · NIBIB · UNIVERSITY OF CALIFORNIA-IRVINE · PI GHOLIPOUR-BABOLI, ALI · 2021 to 2024
$2.2M
Robust quantitative MR imaging markers of response to therapy in Crohn’s DiseaseR01DK125561 · NIDDK · BOSTON CHILDREN'S HOSPITAL · PI KURUGOL, SILA · 2021 to 2025
$2.0M
Advancing Microstructural and Vascular Neuroimaging in Perinatal StrokeR01NS106030 · NINDS · BOSTON CHILDREN'S HOSPITAL · PI WARFIELD, SIMON K · 2019 to 2023
$1.7M
Motion and Distortion Robust Diffusion Weighted Imaging Sequences for Pediatric PatientsR01NS121657 · NINDS · BOSTON CHILDREN'S HOSPITAL · PI Onur Afacan · 2022 to 2026
$1.7M
Machine learning algorithms to analyze large medical image datasetsR01LM013608 · NLM · BOSTON CHILDREN'S HOSPITAL · PI WARFIELD, SIMON K · 2021 to 2024
$1.5M
Ultrasound-based sensors for the fusion and motion correction of MRI and PET/CT dataR01EB030470 · NIBIB · BRIGHAM AND WOMEN'S HOSPITAL · PI MADORE, BRUNO · 2021 to 2021
$1.3M
Machine learning for fast motion compensated quantitative abdominal DCE-MRIR21EB029627 · NIBIB · BOSTON CHILDREN'S HOSPITAL · PI KURUGOL, SILA · 2020 to 2020
$708k
Accurate quantification of kidney function with non-sedated motion-compensated pediatric MRIR21DK123569 · NIDDK · BOSTON CHILDREN'S HOSPITAL · PI KURUGOL, SILA · 2020 to 2021
$487k
NCI NIH HHS R01 CA241817NIBIB NIH HHS R01 EB019483NIBIB NIH HHS R01 EB030470NIBIB NIH HHS R01 EB031849NIBIB NIH HHS R21 EB029627NIDDK NIH HHS R01 DK125561NIDDK NIH HHS R21 DK123569NIH HHS R01CA241817NIH HHS R01DK125561NIH HHS R01EB019483NIH HHS R01EB030470NIH HHS R01EB031849NIH HHS R01LM013608NIH HHS R01NS106030NIH HHS R01NS121657NIH HHS R21DK123569NIH HHS R21EB029627NIH HHS S10OD0250111NINDS NIH HHS R01 NS106030NINDS NIH HHS R01 NS121657NLM NIH HHS R01 LM013608Wellcome TrustWellcome Trust 203139/Z/16/Z
6 · The paper itself

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.

Indexed as

HeartMagnetic Resonance ImagingElectrocardiographyHumansMotionMovementancillary deviceshybrid imagingmotion compensationmotion detectionsensors

Identifiers

PMID36326548
PMCPMC9957809
OpenAlexW4308053981

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.