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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, 2Universität Bremen, Bremen, Germany, 3Fraunhofer MEVIS, Bremen, Germany, 4Darmstadt University of Applied Sciences, Darmstadt, Germany, 5Amsterdam University Medical Center, Amsterdam, Netherlands

Synopsis

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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Keywords