Reinforcement Learning for Automated Reference-free MR Image Quality Assessment

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.