Evidence map›Paper›PMID 40712997›Full record

ArticleNeuroImage2025

Towards quantitative intensity analysis of conventional T1-weighted images in multiple sclerosis.

Tun Wiltgen, Julian McGinnis, Ronja Berg, Cui Ci Voon, Oula Puonti, Katrin Giglhuber, Carl Ganter, Claus Zimmer, Bernhard Hemmer, Benedikt Wiestler and 3 more

Abstract read
In one paragraph

Article in NeuroImage, 2025. 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

13 authors.

Tun WiltgenSchool of Medicine and Health, Department of Neurology, Technical University of Munich, Munich, Germany; School of Medicine and Health, TUM-Neuroimaging Center, Technical University of Munich, Munich, Germany. Electronic address: tun.wiltgen@tum.de.
Julian McGinnisSchool of Medicine and Health, Department of Neurology, Technical University of Munich, Munich, Germany; School of Medicine and Health, TUM-Neuroimaging Center, Technical University of Munich, Munich, Germany; Chair for AI in Healthcare and Medicine, TUM University Hospital, Technical University of Munich, Munich, Germany.
Ronja BergSchool of Medicine and Health, Department of Neurology, Technical University of Munich, Munich, Germany; School of Medicine and Health, TUM-Neuroimaging Center, Technical University of Munich, Munich, Germany; School of Medicine and Health, Institute for Diagnostic and Interventional Neuroradiology, Technical University of Munich, Munich, Germany.
Cui Ci VoonSchool of Medicine and Health, Department of Neurology, Technical University of Munich, Munich, Germany; School of Medicine and Health, TUM-Neuroimaging Center, Technical University of Munich, Munich, Germany.
Oula PuontiDanish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Denmark; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA.
Katrin GiglhuberSchool of Medicine and Health, Department of Neurology, Technical University of Munich, Munich, Germany.
Carl GanterSchool of Medicine and Health, Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany.
Claus ZimmerSchool of Medicine and Health, Institute for Diagnostic and Interventional Neuroradiology, Technical University of Munich, Munich, Germany.
Bernhard HemmerSchool of Medicine and Health, Department of Neurology, Technical University of Munich, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.
Benedikt WiestlerSchool of Medicine and Health, AI for Image-Guided Diagnosis and Therapy, Technical University of Munich, Munich, Germany; Munich Center for Machine Learning (MCML), Munich, Germany.
Jan KirschkeSchool of Medicine and Health, Institute for Diagnostic and Interventional Neuroradiology, Technical University of Munich, Munich, Germany.
Christine PreibischSchool of Medicine and Health, Department of Neurology, Technical University of Munich, Munich, Germany; School of Medicine and Health, TUM-Neuroimaging Center, Technical University of Munich, Munich, Germany; School of Medicine and Health, Institute for Diagnostic and Interventional Neuroradiology, Technical University of Munich, Munich, Germany.
Mark MühlauSchool of Medicine and Health, Department of Neurology, Technical University of Munich, Munich, Germany; School of Medicine and Health, TUM-Neuroimaging Center, Technical University of Munich, Munich, Germany.

Funding

Computational Imaging Biomarkers of Multiple SclerosisR01NS112161 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI VAN LEEMPUT, KOEN · 2019 to 2023
$1.5M
NINDS NIH HHS R01 NS112161
6 · The paper itself

Abstract

Conventional T1-weighted (T1w) magnetic resonance imaging (MRI) is commonly used in multiple sclerosis (MS) morphometry and volumetry research. However, arbitrary intensity scales preclude interpretation of signal values across patients, sites, and time. This requires quantitative MRI techniques, which are not always available. This study assessed T1w image intensity scaling methods, relying on extracerebral reference regions, for quantitative analysis of brain MRI in MS. In total, 701 people with a diagnosis of radiologically isolated syndrome, clinically isolated syndrome, or MS were included. Four intensity scaling strategies were applied: 1) MRI signal modeling, 2) linear scaling with reference regions, 3) z-score standardization, and 4) none (only bias field correction). Methods were evaluated using variance analysis, R1 map comparison, and normal-appearing white matter (NAWM) intensity group comparison, using mean and coefficient of variation (CoV), between low (≤3) and high (>3) expanded disability status scale (EDSS) scores. Statistical analysis was conducted using Pearson's r, two-sided Welch two-sample t-test, ANCOVA, and Cohen's d. Linear scaling with temporal fatty tissue achieved the most consistent variance reduction and strong correlation with R1 maps (r = 0.84). R1 values in NAWM were significantly lower in people with high compared to low EDSS scores (d = -0.351). Similarly, group differences in mean NAWM intensity of fat-scaled images were significant (d = -0.252). The largest group differences were found in NAWM CoV in bias field-corrected T1w images (d = 0.818). Linear scaling with fatty tissue most accurately reproduced the results obtained with R1 maps. Changes in MS NAWM appear to increase intensity variability detectable in conventional T1w images.

Indexed as

BrainImage Interpretation, Computer-AssistedMagnetic Resonance ImagingMultiple SclerosisWhite MatterAdultFemaleHumansImage Processing, Computer-AssistedMaleMiddle AgedYoung AdultIntensity scalingMultiple sclerosisNormal-appearing white matterQuantitative MRIT1-weighted MRI

Identifiers

PMID40712997
PMCPMC13523021

What OpenQuestion holds

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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.