Comparing DSC-CBV, DSC-CBF and ASL for Detecting Residual and Recurrent Glioblastoma with Deep Learning and multishell Diffusion MRI

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.