Evidence map›Paper›PMID 40312865›Full record

ArticleMagnetic resonance in medicine2025

Quantitative susceptibility mapping in magnetically inhomogeneous tissues.

Thomas Jochmann, Fahad Salman, Michael G Dwyer, Niels Bergsland, Robert Zivadinov, Jens Haueisen, Ferdinand Schweser

Abstract read
In one paragraph

Article in Magnetic resonance in medicine, 2025. 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

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

7 authors.

Thomas JochmannInstitute of Biomedical Engineering and Informatics, Department of Computer Science and Automation, Technische Universität Ilmenau, Ilmenau, Germany.ORCID https://orcid.org/0000-0002-3564-4127
Fahad SalmanBuffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, The State University of New York, Buffalo, New York, USA.ORCID https://orcid.org/0009-0000-3873-2301
Michael G DwyerBuffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, The State University of New York, Buffalo, New York, USA.
Niels BergslandBuffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, The State University of New York, Buffalo, New York, USA.ORCID https://orcid.org/0000-0002-7792-0433
Robert ZivadinovBuffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, The State University of New York, Buffalo, New York, USA.
Jens HaueisenInstitute of Biomedical Engineering and Informatics, Department of Computer Science and Automation, Technische Universität Ilmenau, Ilmenau, Germany.ORCID https://orcid.org/0000-0003-3871-2890
Ferdinand SchweserBuffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, The State University of New York, Buffalo, New York, USA.ORCID https://orcid.org/0000-0003-0399-9211

Funding

University of Buffalo Clinical and Translational Science Institute - Supplement SchulyerUL1TR001412 · NCATS · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI MURPHY, TIMOTHY F · 2015 to 2024
$33.8M
Deep Gray Matter Iron and Disease Progression in Multiple SclerosisR01NS114227 · NINDS · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI SCHWESER, FERDINAND · 2021 to 2024
$1.5M
Acquisition of High Performance Data and Computing Infrastructure to Advance Biomedical ResearchS10OD024973 · OD · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI FURLANI, THOMAS · 2019 to 2019
$1.0M
Bundesministerium für Bildung und Forschung 16KISA024Deutscher Akademischer Austauschdienst PPP 57599925Freistaat Thüringen 2018 IZN 0004International Society for Magnetic Resonance in MedicineNational Center for Advancing Translational Sciences of the National Institutes of Health UL1TR001412National Institute of Neurological Disorders and Stroke of the National Institutes of Health R01NS114227National Science Foundation 1724891NCATS NIH HHS UL1 TR001412NIH HHS S10 OD024973NIH HHS S10OD024973NINDS NIH HHS R01 NS114227
6 · The paper itself

Abstract

purposeConventional quantitative susceptibility mapping (QSM) methods rely on simplified physical models that assume isotropic and homogeneous tissue properties, leading to artifacts and inaccuracies in biological tissues. This study aims to develop and evaluate DEEPOLE, a deep learning-based method that incorporates macroscopically nondipolar Larmor frequency shifts into QSM to enhance the quality and accuracy of susceptibility maps.

methodsDEEPOLE integrates the QUASAR model into a deep convolutional neural network to account for frequency contributions neglected by conventional QSM. We trained DEEPOLE using synthesized data reflecting realistic power spectrum distributions. Its performance was evaluated against traditional QSM algorithms-including deep learning QSM, QUASAR (quantitative susceptibility and residual mapping), morphology-enabled dipole inversion (MEDI), fast nonlinear susceptibility inversion (FANSI), and superfast dipole inversion (SDI)-using realistic digital brain models with and without microstructure effects, as well as in vivo human brain data. Quantitative assessments focused on susceptibility estimation accuracy, artifact reduction, and anatomical consistency.

resultsIn digital brain models, DEEPOLE outperformed conventional QSM methods by producing susceptibility maps with fewer artifacts and greater quantitative accuracy, especially in regions affected by microstructure effects. In vivo, DEEPOLE generated more anatomically consistent susceptibility maps and mitigated artifacts such as inhomogeneities and streaking, providing improved susceptibility estimates in deep gray matter and white matter.

conclusionIncorporating macroscopically nondipolar Larmor frequency shifts into QSM through DEEPOLE improves the quality and accuracy of susceptibility maps. This methodological advancement enhances the reliability of susceptibility measurements, particularly in studies of neurodegenerative and demyelinating conditions where macroscopically nondipolar contributions are substantial.

Indexed as

BrainImage Processing, Computer-AssistedMagnetic Resonance ImagingAlgorithmsArtifactsDeep LearningGray MatterHumansNeural Networks, ComputerReproducibility of ResultsWhite Matterchemical exchangemacroscopically nondipolar Larmor frequency shiftsmicrostructurephase contrastquantitative susceptibility mapping

Identifiers

PMID40312865
PMCPMC12202724

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.