Evidence map›Paper›PMID 39375036›Full record

ReviewNMR in biomedicine2025

MRI at low field: A review of software solutions for improving SNR.

Reina Ayde, Marc Vornehm, Yujiao Zhao, Florian Knoll, Ed X Wu, Mathieu Sarracanie

Abstract readReview
In one paragraph

Review in NMR in biomedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

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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

6 authors.

Reina AydeCenter for Adaptable MRI Technology, Institute of Medical Sciences, School of Medicine & Nutrition, University of Aberdeen, Aberdeen, UK.ORCID 0000-0003-3085-6240
Marc VornehmDepartment of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Yujiao ZhaoDepartment of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong, China.
Florian KnollDepartment of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Ed X WuDepartment of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong, China.ORCID 0000-0001-5581-1546
Mathieu SarracanieCenter for Adaptable MRI Technology, Institute of Medical Sciences, School of Medicine & Nutrition, University of Aberdeen, Aberdeen, UK.

Funding

German Research Foundation 500888779German Research Foundation 512819079German Research Foundation 513220538Hong Kong Research Grant Council HKU17112120Hong Kong Research Grant Council HKU17125624Hong Kong Research Grant Council HKU17127022Hong Kong Research Grant Council HKU17127121Hong Kong Research Grant Council HKU17127523Hong Kong Research Grant Council HKU7119224Hong Kong Research Grant Council R7003-19FSwiss National Science Foundation PCEFP2_186861
6 · The paper itself

Abstract

Low magnetic field magnetic resonance imaging (MRI) (

Indexed as

Magnetic Resonance ImagingSignal-To-Noise RatioSoftwareHumansdeep learningelectromagnetic interference cancellationimage enhancementk‐space samplinglow‐field MRIlow signal‐to‐noise ratio (SNR)

Identifiers

PMID39375036
PMCPMC11605168

What OpenQuestion holds

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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.