The presented neural network with 3D-FiLM-cGAN architecture synthesizes cerebral blood flow maps from T1-weighted input images. Acquisition- and subject-specific metadata such as sex, arterial spin labeling (ASL) method and readout techniques were fed into the neural network as auxiliary input. The multi-vendor database including different ASL sequence types was created from ADNI data which were preprocessed in ExploreASL and transformed to MNI standard space. A subset of data from a single vendor (GE) were used for supervised training exemplarily and compared to CBF from acquired ASL data.
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