Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.06.01.126375v1?rss=1 Authors: Nalawade, S., Yu, F. F., Bangalore Yogananda, C. G., Murugesan, G. K., Shah, B. R., Pinho, M. C., Wagner, B. C., Mickey, B., Patel, T. R., Fei, B., Madhuranthakam, A. J., Maldjian, J. A. Abstract: Deep learning has shown promise for predicting glioma molecular profiles using MR images. Before clinical implementation, ensuring robustness to real-world problems, such as patient motion, is crucial. We sought to evaluate the effects of motion artifact on glioma marker classifier performance and develop a deep learning motion correction network to restore classification accuracies. T2w images and molecular information were retrieved from the TCIA and TCGA databases. Three-fold cross-validation was used to train and test the motion correction network on artifact-corrupted images. We then compared the performance of three glioma marker classifiers (IDH mutation, 1p/19q codeletion, and MGMT methylation) using motion-corrupted and motion-corrected images. Glioma marker classifier performance decreased markedly with increasing motion corruption. Applying motion correction effectively restored classification accuracy for even the most motion-corrupted images. Robust motion correction can enable high accuracy in deep learning MRI-based molecular marker classification rivaling tissue-based characterization. Copy rights belong to original authors. Visit the link for more info