Ramy Hussein1, Moss Zhao1, Jia Guo2, Kevin Chen1, David Shin3, Michael Moseley1, and Greg Zaharchuk1
1Radiology, Stanford University, Stanford, CA, United States, 2Bioengineering, University of California, Riverside, Riverside, CA, United States, 3Neuro MR, GE Healthcare, Menlo Park, CA, United States
1Radiology, Stanford University, Stanford, CA, United States, 2Bioengineering, University of California, Riverside, Riverside, CA, United States, 3Neuro MR, GE Healthcare, Menlo Park, CA, United States
This work demonstrates that a 3D convolutional encoder-decoder network integrating multi-contrast information from brain structural MRI and ASL perfusion images can synthesize high-quality PET CBF maps.
Figure 3. Examples of reference PET CBF and corresponding synthetic CBF maps generated with different loss functions and network settings.
Figure 1. Our 3D convolutional encoder-decoder network for predicting PET CBF maps from multi-contrast MRI scans.
