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Abstract #0298

Acquisition Parameter Conditioned Generative Adversarial Network for Enhanced MR Image Synthesis

Jonas Denck1,2,3, George William Ferguson3, Jens Guehring3, Andreas Maier1, and Eva Rothgang2
1Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany, 2Technical University of Applied Sciences Amberg-Weiden, Amberg, Germany, 3Siemens Healthineers, Erlangen, Germany

Current approaches for the synthesis of MR images are only trained on MR images with a specific set of acquisition parameter values, limiting the clinical value of these methods. We therefore trained a generative adversarial network (GAN) to generate synthetic MR knee images conditioned on various acquisition parameters (TR, TE, imaging orientation). This enables us to synthesize MR images with adjustable image contrast. This work can support radiologists and technologists during the parameterization of MR sequences, can serve as a valuable tool for radiology training, and can be used for customized data generation to support AI training.

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