Analyzing the effects of free water modeling by deep learning on diffusion MRI structural connectivity estimates in glioma patients
Weninger L, Na CH, Juetten K, Merhof D (2020)
Publication Type: Journal article
Publication year: 2020
Journal
Book Volume: 15
Article Number: e0239475
Journal Issue: 9 September
DOI: 10.1371/journal.pone.0239475
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.
Involved external institutions
Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen
Germany (DE)
Universitätsklinikum Aachen (UKA)
Germany (DE)
Germany (DE)
Universitätsklinikum Aachen (UKA)
Germany (DE)
How to cite
APA:
Weninger, L., Na, C.-H., Juetten, K., & Merhof, D. (2020). Analyzing the effects of free water modeling by deep learning on diffusion MRI structural connectivity estimates in glioma patients. PLoS ONE, 15(9 September). https://doi.org/10.1371/journal.pone.0239475
MLA:
Weninger, Leon, et al. "Analyzing the effects of free water modeling by deep learning on diffusion MRI structural connectivity estimates in glioma patients." PLoS ONE 15.9 September (2020).
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