Evidence map›Paper›PMID 32976545›Full record

ArticlePloS one2020

Analyzing the effects of free water modeling by deep learning on diffusion MRI structural connectivity estimates in glioma patients.

Leon Weninger, Chuh-Hyoun Na, Kerstin Jütten, Dorit Merhof

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.6field-weighted citation impact, top 29% of its field
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

9 citing papers in PubMed, 9 citations in OpenAlex.

  1. Impact of Free Water Correction on the Mean Diffusivity of Glioblastoma is Regional and b-value Dependent: A Retrospective Study on Open-source Datasets.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026
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  5. Learning-based Free-Water Correction using Single-shell Diffusion MRI.Proceedings of SPIE--the International Society for Optical Engineering · 2024
    Article
  6. Review
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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

4 authors at 1 institution in 1 country.

Leon WeningerImaging & Computer Vision, RWTH Aachen University, Aachen, Germany.ORCID 0000-0002-7263-4219
Chuh-Hyoun NaDepartment of Neurosurgery, University Hospital RWTH Aachen, Aachen, Germany.
Kerstin JüttenDepartment of Neurosurgery, University Hospital RWTH Aachen, Aachen, Germany.ORCID 0000-0002-9024-1849
Dorit MerhofImaging & Computer Vision, RWTH Aachen University, Aachen, Germany.
RWTH Aachen University · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diffusion-weighted MRI makes it possible to quantify subvoxel brain microstructure and to reconstruct white matter fiber trajectories with which structural connectomes can be created. However, at the border between cerebrospinal fluid and white matter, or in the presence of edema, the obtained MRI signal originates from both the cerebrospinal fluid as well as from the white matter partial volume. Diffusion tractography can be strongly influenced by these free water partial volume effects. Thus, including a free water model can improve diffusion tractography in glioma patients. Here, we analyze how including a free water model influences structural connectivity estimates in healthy subjects as well as in brain tumor patients. During a clinical study, we acquired diffusion MRI data of 35 glioma patients and 28 age- and sex-matched controls, on which we applied an open-source deep learning based free water model. We performed deterministic as well as probabilistic tractography before and after free water modeling, and utilized the tractograms to create structural connectomes. Finally, we performed a quantitative analysis of the connectivity matrices. In our experiments, the number of tracked diffusion streamlines increased by 13% for high grade glioma patients, 9.25% for low grade glioma, and 7.65% for healthy controls. Intra-subject similarity of hemispheres increased significantly for the patient as well as for the control group, with larger effects observed in the patient group. Furthermore, inter-subject differences in connectivity between brain tumor patients and healthy subjects were reduced when including free water modeling. Our results indicate that free water modeling increases the similarity of connectivity matrices in brain tumor patients, while the observed effects are less pronounced in healthy subjects. As the similarity between brain tumor patients and healthy controls also increased, connectivity changes in brain tumor patients may have been overestimated in studies that did not perform free water modeling.

Indexed as

Diffusion Magnetic Resonance ImagingAdolescentAdultAgedAged, 80 and overBrain NeoplasmsConnectomeDeep LearningFemaleGliomaHumansImage Processing, Computer-AssistedMaleMiddle AgedProspective StudiesWaterWater

Identifiers

PMID32976545
PMCPMC7518620
OpenAlexW3089032154

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LicenceCC BY
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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.