Abstract #3979

Automated reference-free assessment of MR image quality using an active learning approach: Comparison of Support Vector Machine versus Deep Neural Network classification.

Sergios Gatidis¹, Annika Liebgott¹, Martin Schwartz¹, Petros Martirosian¹, Fritz Schick¹, Konstantin Nikolaou¹, Bin Yang², and Thomas Küstner^1,2

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

In this study we compare the performance of Support Vector Machine (SVM)-based and Deep Neural Network (DNN)-based active learning for automated assessment of MR image quality. MR images were labeled by radiologists concerning perceived image quality and used as training and test data. DNN and SVM were trained to classify image quality on the training data. An active learning scheme was used for optimization of the training procedure. We found that using acitve learning with either SVM- or DNN- based classification allows for accurate and efficient automated assessment of MR image quality.

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