Keywords: Tumors (Post-Treatment), Tumor
Motivation: Differentiating recurrent tumor from post-treatment changes is challenging in post-operative glioblastoma MRI.
Goal(s): To compare the performance of DSC-CBV, DSC-CBF, and ASL perfusion MRI to differentiate recurrent tumor from treatment-related changes using a Deep Learning segmentation model together with multishell Diffusion MRI.
Approach: 138 post-operative scans were manually segmented for enhancing and non-enhancing cellular tumor volume. A Deep Learning segmentation was trained to segment cellular tumor and then tested to differentiate recurring disease from post-treatment changes from the segmentations.
Results: DSC-CBV and DSC-CBF improved the detection of residual/recurrent cellular tumor with Deep Learning while ASL perfusion did not.
Impact: Our work re-demonstrates the importance of including a DSC perfusion method in clinical brain tumor MRI protocols.
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