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Abstract #4848

Reinforcement Learning for Automated Reference-free MR Image Quality Assessment

Annika Liebgott1,2, Jianming Yi2, Thomas Küstner1,2,3, Konstantin Nikolaou1, Bin Yang2, and Sergios Gatidis1

1Diagnostic and Interventional Radiology, University Hospital of Tuebingen, Tübingen, Germany, 2Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany, 3School of Biomedical Engineering and Imaging Sciences, King's College London/St Thomas' Hospital, London, United Kingdom

Reinforcement learning is a method aiming to model a learner similar to human learning behavior. In this study, we investigate the possibility to utilize this technique to select an optimal feature set for automated reference-free MR image quality assessment. In our proposed setup, we use Q-learning and a random forest classifier to provide feedback to the learner. Moreover, we investigate a combination of multiple reinforcement learning models. Results show that our random-forest-based reinforcement learning setup can achieve higher accuracies than the previously used support vector machines or feature-based deep neural networks combined with traditional feature reduction like PCA.

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