Abstract #4387
Hierarchical non-negative matrix factorization using multi-parametric MRI to assess tumor heterogeneity within gliomas.
Nicolas Sauwen 1,2 , Diana Sima 1,2 , Sofie Van Cauter 3 , Jelle Veraart 4,5 , Alexander Leemans 6 , Frederik Maes 1,2 , Uwe Himmelreich 7 , and Sabine Van Huffel 1,2
1
Department of Electrical Engineering (ESAT),
KU Leuven, Leuven, Belgium,
2
iMinds
Medical IT, Leuven, Belgium,
3
Department
of Radiology, University Hospitals of Leuven, Leuven,
Belgium,
4
iMinds
Vision Lab, Department of Physics, University of
Antwerp, Antwerp, Belgium,
5
Center
for Biomedical Imaging, Department of Radiology, New
York University Langone Medical Center, New York, NY,
United States,
6
Image
Sciences Institute, University Medical Center Utrecht,
Utrecht University, Utrecht, Netherlands,
7
Biomedical
MRI/MoSAIC, Department of Imaging and Pathology, KU
Leuven, Leuven, Belgium
Tissue characterization within gliomas is challenging
due to the co-existence of several intra-tumoral tissue
types and the high spatial heterogeneity in high-grade
gliomas. An accurate and reproducible method for brain
tumor characterization and the detection of relevant
tumor substructures could be of great added value for
tumor diagnosis, treatment planning and follow-up. In
this study, a hierarchical non-negative matrix
factorization (hNMF) technique is applied to
multi-parametric MRI data of 24 glioma patients. hNMF
can be applied on a patient-by-patient basis, it does
not require large training datasets and it provides a
more refined voxelwise tissue characterization compared
to binary classification.
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