Tissue classification of cerebral gliomas using MR fingerprinting signal and deep learning
Yong Chen1, Rasim Boyacioglu1, Gamage Sugandima Nishadi Weragoda2, Michael Martens2, Mark Griswold1, and Chaitra Badve1,3
1Radiology, Case Western Reserve University, Cleveland, OH, United States, 2Physics, Case Western Reserve University, Cleveland, OH, United States, 3Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
In this pilot study, we aim to analyze MR Fingerprinting (MRF) signal using deep learning network to assess the performance of tissue classification in gliomas. A U-Net based convolutional neural network was trained to learn glioma grades based on the SVD-compressed fingerprint acquired using MRF. Based on data acquired from a 5-minute MRF scan, the method shows great potential to accurately classify glioma grades without the need of image registration and contrast administration.
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