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Verfasst von:Hahn, Artur [VerfasserIn]   i
 Krüwel-Bode, Julia [VerfasserIn]   i
 Schuhegger, Sarah [VerfasserIn]   i
 Krüwel, Thomas [VerfasserIn]   i
 Sturm, Volker Jörg Friedrich [VerfasserIn]   i
 Zhang, Ke [VerfasserIn]   i
 Jende, Johann [VerfasserIn]   i
 Tews, Björn [VerfasserIn]   i
 Heiland, Sabine [VerfasserIn]   i
 Bendszus, Martin [VerfasserIn]   i
 Breckwoldt, Michael O. [VerfasserIn]   i
 Ziener, Christian H. [VerfasserIn]   i
 Kurz, Felix T. [VerfasserIn]   i
Titel:Brain tumor classification of virtual NMR voxels based on realistic blood vessel-induced spin dephasing using support vector machines
Verf.angabe:Artur Hahn, Julia Bode, Sarah Schuhegger, Thomas Krüwel, Volker J.F. Sturm, Ke Zhang, Johann M.E. Jende, Björn Tews, Sabine Heiland, Martin Bendszus, Michael O. Breckwoldt, Christian H. Ziener, Felix T. Kurz
E-Jahr:2020
Jahr:14 April 2020
Umfang:17 S.
Fussnoten:Gesehen am 12.08.2021
Titel Quelle:Enthalten in: NMR in biomedicine
Ort Quelle:New York, NY : Wiley, 1988
Jahr Quelle:2020
Band/Heft Quelle:(2020), Artikel-ID e4307$nSpecial issue, Seite 1-17
ISSN Quelle:1099-1492
Abstract:Remodeling of tissue microvasculature commonly promotes neoplastic growth; however, there is no imaging modality in oncology yet that noninvasively quantifies microvascular changes in clinical routine. Although blood capillaries cannot be resolved in typical magnetic resonance imaging (MRI) measurements, their geometry and distribution influence the integral nuclear magnetic resonance (NMR) signal from each macroscopic MRI voxel. We have numerically simulated the expected transverse relaxation in NMR voxels with different dimensions based on the realistic microvasculature in healthy and tumor-bearing mouse brains (U87 and GL261 glioblastoma). The 3D capillary structure in entire, undissected brains was acquired using light sheet fluorescence microscopy to produce large datasets of the highly resolved cerebrovasculature. Using this data, we trained support vector machines to classify virtual NMR voxels with different dimensions based on the simulated spin dephasing accountable to field inhomogeneities caused by the underlying vasculature. In prediction tests with previously blinded virtual voxels from healthy brain tissue and GL261 tumors, stable classification accuracies above 95% were reached. Our results indicate that high classification accuracies can be stably attained with achievable training set sizes and that larger MRI voxels facilitated increasingly successful classifications, even with small training datasets. We were able to prove that, theoretically, the transverse relaxation process can be harnessed to learn endogenous contrasts for single voxel tissue type classifications on tailored MRI acquisitions. If translatable to experimental MRI, this may augment diagnostic imaging in oncology with automated voxel-by-voxel signal interpretation to detect vascular pathologies.
DOI:doi:10.1002/nbm.4307
URL:Volltext: https://doi.org/10.1002/nbm.4307
 Volltext: https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/nbm.4307
 DOI: https://doi.org/10.1002/nbm.4307
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:angiogenesis
 glioblastoma multiforme
 machine learning
 microvasculature
 signal classification
 spin dephasing
 support vector machines
 vascular pathology
K10plus-PPN:1766718817
Verknüpfungen:→ Zeitschrift
 
 
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