Abstract #0466

Generation of globally consistent non-confidential MRI data using deep generative adversarial networks

Karim Armanious^1,2, Mario Döbler¹, Bin Yang¹, and Sergios Gatidis²

¹Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany, ²Radiology Department, University Hospital Tübingen, Tübingen, Germany

The lack of easily accessible open-source medical datasets is one of the biggest limiting factors to advance the development of deep models for any medical task. In this work, we utilize progressive growing of generative adversarial networks to generate high-resolution, realistic, non-confidential MRI data. Additionally, self-attention is used to model long-range dependencies to improve global consistency of the generated data. For a feasibility study, our models are trained on MRI data of the head region and are evaluated qualitatively and quantitatively. The results illustrate the capability of the proposed model to generate MRI data.

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