Evidence map›Paper›PMID 38199994›Full record

ArticleNature communications2024

Single-sided magnetic resonance-based sensor for point-of-care evaluation of muscle.

Sydney E Sherman, Alexa S Zammit, Won-Seok Heo, Matthew S Rosen, Michael J Cima

Abstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Sydney E ShermanHarvard-MIT Program in Health Science and Technology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.ORCID 0009-0006-5408-1872
Alexa S ZammitKoch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Won-Seok HeoKoch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.ORCID 0000-0001-7015-111X
Matthew S RosenDepartment of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, 02129, USA.ORCID 0000-0002-7194-002X
Michael J CimaKoch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. mjcima@mit.edu.ORCID 0000-0003-2379-6139

Funding

NMR-Based Rapid Fluid Assessment: Device Design and Signal ProcessingR01EB031813 · NIBIB · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI CIMA, MICHAEL J, NIGWEKAR, SAGAR · 2022 to 2025
$2.2M
NIBIB NIH HHS R01 EB031813
6 · The paper itself

Abstract

Magnetic resonance imaging is a widespread clinical tool for the detection of soft tissue morphology and pathology. However, the clinical deployment of magnetic resonance imaging scanners is ultimately limited by size, cost, and space constraints. Here, we discuss the design and performance of a low-field single-sided magnetic resonance sensor intended for point-of-care evaluation of skeletal muscle in vivo. The 11 kg sensor has a penetration depth of >8 mm, which allows for an accurate analysis of muscle tissue and can avoid signal from more proximal layers, including subcutaneous adipose tissue. Low operational power and shielding requirements are achieved through the design of a permanent magnet array and surface transceiver coil. The sensor can acquire high signal-to-noise measurements in minutes, making it practical as a point-of-care tool for many quantitative diagnostic measurements, including T2 relaxometry. In this work, we present the in vitro and human in vivo performance of the device for muscle tissue evaluation.

Indexed as

Magnetic Resonance ImagingPoint-of-Care SystemsHumansMagnetic Resonance SpectroscopyMuscle, SkeletalSubcutaneous Fat

Identifiers

PMID38199994
PMCPMC10782019

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.