Evidence map›Paper›PMID 42529368›Full record

ReviewFrontiers in public health2026

Multidimensional correlation magnetic resonance imaging in low- and middle-income countries: opportunities and barriers to equitable deployment.

Valentine Terfa Genyi, Bamidele Omotayo Awojoyogbe, Michael Oluwaseun Dada, Oyeleke Ismail Olarinoye, Henry Ananyi Sanni

Abstract readReview
In one paragraph

Review in Frontiers in public health, 2026. 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
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Valentine Terfa GenyiDepartment of Physics, Federal University of Technology, Minna, Niger State, Nigeria.
Bamidele Omotayo AwojoyogbeDepartment of Physics, Federal University of Technology, Minna, Niger State, Nigeria.
Michael Oluwaseun DadaDepartment of Physics, Federal University of Technology, Minna, Niger State, Nigeria.
Oyeleke Ismail OlarinoyeDepartment of Physics, Federal University of Technology, Minna, Niger State, Nigeria.
Henry Ananyi SanniDepartment of Physics, Federal University of Technology, Minna, Niger State, Nigeria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multidimensional Correlation Magnetic Resonance Imaging (McMRI) represents a promising advancement in medical imaging, offering enhanced diagnostic accuracy through the integration of multiple MRI parameters into unified datasets. In low- and middle-income countries (LMICs), McMRI could significantly improve early disease detection and treatment planning, particularly for complex conditions such as cancer and neurological disorders. Benefits include richer data acquisition, improved tissue characterization, and the potential for cost-effective, non-invasive diagnostics. However, practical barriers remain: limited infrastructure, scarcity of trained personnel, high implementation costs, unstable power supply, and data governance challenges hinder widespread adoption. Addressing these issues requires efficient computational workflows, sustainable technology investment, and capacity-building initiatives tailored to LMIC contexts. This review highlights both the transformative potential and the practical obstacles of deploying McMRI in resource-constrained settings, underscoring the need for collaborative strategies that align technological innovation with healthcare equity.

Indexed as

Developing CountriesMagnetic Resonance ImagingHumansResource-Limited Settingschallenges and benefitscomputational techniquesdiagnostic imaginglow- and middle-income countries (LMICs)multidimensional correlation MRIpattern recognition

Identifiers

PMID42529368
PMCPMC13416859

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Registered trials

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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.