Evidence map›Paper›PMID 42552584›Full record

ArticleMagnetic resonance in medicine2026

Deep Learning Improves Robustness of Voxelwise Kinetic Modeling for Hyperpolarized Carbon-13 MRI.

Kofi Deh, Yeona Kang, Tsang-Wei Tu, Rania Jones, Curtiland Deville, Rao Khan

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Article in Magnetic resonance in medicine, 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
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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

6 authors.

Kofi DehDepartment of Physics and Astronomy, Howard University, Washington, DC, USA.
Yeona KangDepartment of Mathematics, Howard University, Washington, DC, USA.
Tsang-Wei TuCollege of Medicine, Howard University, Washington, DC, USA.
Rania JonesDepartment of Physics and Astronomy, Howard University, Washington, DC, USA.
Curtiland DevilleDepartment of Radiation Oncology, Johns Hopkins Medicine, Washington, DC, USA.
Rao KhanDepartment of Physics and Astronomy, Howard University, Washington, DC, USA.

Funding

American Cancer Society DICRIDG-21-074-01-DICRIDGNIH HHS 1U54CA295337-01
6 · The paper itself

Abstract

purposeTo evaluate whether deep learning improves the robustness of voxelwise kinetic parameter estimation from hyperpolarized (HP)

methodsA hybrid neural network (NN) was trained on synthetic pyruvate/lactate time courses generated from an open-system two-compartment HP

resultsIn simulations, NLLS and NN performance were comparable for

conclusionNLLS is effective for

Indexed as

Carbon IsotopesDeep LearningImage Processing, Computer-AssistedMagnetic Resonance ImagingAlgorithmsAnimalsComputer SimulationHumansKineticsLactic AcidLeast-Squares AnalysisNeural Networks, ComputerPyruvic AcidSignal-To-Noise RatioCarbon-13Carbon IsotopesLactic AcidPyruvic Acidhyperpolarized carbon‐13 MRIkinetic modelingneural networks

Identifiers

PMID42552584
PMCPMC13619905

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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.