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Automated quantitative evaluation of deep learning model for reduced gadolinium dose in contrast-enhanced brain MRI
Srivathsa Pasumarthi1, Jon Tamir2, Enhao Gong2, Greg Zaharchuk2, and Tao Zhang2
1Subtle Medical Inc, Menlo Park, CA, United States, 2Subtle Medical Inc., Menlo Park, CA, United States
This work proposes an automated quantitative evaluation scheme for the GBCA dose reduction using DL.
Overall processing pipeline for the quantitative evaluation scheme. The post-contrast 3D T1W (CE) and DL-CE volumes were skull-stripped, interpolated and co-registered to anatomical template. The volumes are then processed with the BRATS pre-trained volume through the NGC interface to obtain the Tumor Core (TC) segmentation masks.
CE and DL-CE shown side-by-side with the segmented Tumor Core (TC) (green overlay). Individual Dice scores are shown below each image pair. The segmentation mask of DL-CE in image (F) is different from that of CE, even though the enhancement patterns look similar.