Evidence map›Paper›PMID 37502626›Full record

ArticleArXiv2023

Dual-tuned Coaxial-transmission-line RF coils for Hyperpolarized

Komlan Payne, Yunkun Zhao, Aditya Ashok Bhosale, Xiaoliang Zhang

Open access · goldAbstract readPreprint
In one paragraph

Article in ArXiv, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.1field-weighted citation impact, top 55% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Komlan PayneDepartment of Biomedical Engineering, State University of New York at Buffalo, Buffalo, NY 14260 USA.
Yunkun ZhaoDepartment of Biomedical Engineering, State University of New York at Buffalo, Buffalo, NY 14260 USA.
Aditya Ashok BhosaleDepartment of Biomedical Engineering, State University of New York at Buffalo, Buffalo, NY 14260 USA.
Xiaoliang ZhangDepartments of Biomedical Engineering and Electrical Engineering, State University of New York at Buffalo, Buffalo, NY 14260 USA.
University at Buffalo, State University of New York · US

Funding

Enhanced MR for morphological characterization of ligaments, tendons and boneU01EB023829 · NIBIB · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI LI, XIAOJUAN, ZHANG, XIAOLIANG · 2018 to 2024
$1.9M
NIBIB NIH HHS U01 EB023829
6 · The paper itself

Abstract

Objective: Information on the metabolism of tissues in healthy and diseased states plays a significant role in the detection and understanding of tumors, neurodegenerative diseases, diabetes, and other metabolic disorders. Hyperpolarized carbon-13 magnetic resonance imaging ( Methods: Here, we report a dual-tuned coaxial transmission line (CTL) RF coil agile for metabolite information operating at 7T with independent tuning capability. The design analysis has demonstrated how both resonant frequencies can be individually controlled by simply varying the constituent of the design parameters. Results: Numerical results have demonstrated a broadband tuning range capability, covering most of the X-nucleus signal, especially the Conclusion: The proposed dual-tuned RF coils reveal highly effective magnetic field obtained from both proton and heteronuclear signal which is crucial for accurate and detailed imaging. Significance: The successful development of this new dual-tuned RF coil technique would provide a tangible and efficient tool for ultrahigh field metabolic MR imaging.

Indexed as

Brain glucose metabolismsdeuterium magnetic resonance spectroscopyHigh/low-impedance RF coilhyperpolarized 13Cmetabolic imagingultra-high field

Identifiers

PMID37502626
PMCPMC10370217
OpenAlexW4385209764

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.