Evidence map›Paper›PMID 39717463›Full record

ArticleResearch (Washington, D.C.)2024

Conformal Metamaterials with Active Tunability and Self-Adaptivity for Magnetic Resonance Imaging.

Ke Wu, Xia Zhu, Xiaoguang Zhao, Stephan W Anderson, Xin Zhang

Abstract read
In one paragraph

Article in Research (Washington, D.C.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Article
  2. Metamaterial Antennas Enhance MRI of the Eye and Occipital Brain.Advanced materials (Deerfield Beach, Fla.) · 2026
    Article
  3. Article
  4. 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

5 authors.

Ke WuDepartment of Mechanical Engineering, Boston University, Boston, MA 02215, USA.
Xia ZhuDepartment of Mechanical Engineering, Boston University, Boston, MA 02215, USA.
Xiaoguang ZhaoPhotonics Center, Boston University, Boston, MA 02215, USA.
Stephan W AndersonPhotonics Center, Boston University, Boston, MA 02215, USA.
Xin ZhangDepartment of Mechanical Engineering, Boston University, Boston, MA 02215, USA.ORCID https://orcid.org/0000-0002-4413-5084

Funding

Metamaterial-Enabled magnetic Resonance Imaging EnhancementR21EB024673 · NIBIB · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI ANDERSON, STEPHAN WILLIAM, ZHANG, XIN · 2018 to 2020
$660k
NIBIB NIH HHS R21 EB024673
6 · The paper itself

Abstract

Metamaterials hold great potential to enhance the imaging performance of magnetic resonance imaging (MRI) as auxiliary devices, due to their unique ability to confine and enhance electromagnetic fields. Despite their promise, the current implementation of metamaterials faces obstacles for practical clinical adoption due to several notable limitations, including their bulky and rigid structures, deviations from optimal resonance frequency, and inevitable interference with the radiofrequency (RF) transmission field in MRI. Herein, we address these restrictions by introducing a flexible and smart metamaterial that enhances sensitivity by conforming to patient anatomies while ensuring comfort during MRI procedures. The proposed metamaterial selectively amplifies the magnetic field during the RF reception phase by passively sensing the excitation signal strength, remaining "off" during the RF transmission phase. Additionally, the metamaterial can be readily tuned to achieve a precise frequency match with the MRI system through a controlling circuit. The metamaterial presented here paves the way for the widespread utilization of metamaterials in clinical MRI, thereby translating this promising technology to the MRI bedside.

Identifiers

PMID39717463
PMCPMC11665932

What OpenQuestion holds

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LicenceCC BY
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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.