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Abstract #4815

A 3D-FiLM-cGAN Architecture for the Synthesis of Cerebral Blood Flow Maps

Michael Stritt1, Matthias Günther1,2,3, Johannes Gregori1,4, Daniel Mensing1, Henk-Jan Mutsaerts5, and Klaus Eickel1,3
1mediri GmbH, Heidelberg, Germany, 2Universit├Ąt Bremen, Bremen, Germany, 3Fraunhofer MEVIS, Bremen, Germany, 4Darmstadt University of Applied Sciences, Darmstadt, Germany, 5Amsterdam University Medical Center, Amsterdam, Netherlands


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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