Deep-Learning-based contrast synthesis from MRF parameter maps in the knee
Olli Nykänen1,2, Antti Isosalo1, Satu I Inkinen1, Victor Casula1,3, Mika Nevalainen1,3,4, Riccardo Lattanzi5, Martijn Cloos6, Mikko J Nissi2, and Miika T Nieminen1,3,4
1Research Unit of Medical Imaging, Physics and Technology,, University of Oulu, Oulu, Finland, 2Department of Applied Physics, University of Eastern Finland, Kuopio, Finland, 3Medical Research Center, University of Oulu and Oulu University Hospital, Oulu, Finland, 4Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland, 5Center for Advanced Imaging Innovation and Research (CAI2R) and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 6Centre for Advanced Imaging, Queensland University, Brisbane, Australia
In this study, deep convolutional neural networks (DCNN) are used to synthesize contrast-weighted magnetic resonance (MR) images from quantitative parameter maps of the knee joint obtained with magnetic resonance fingerprinting (MRF). Training of the neural networks was performed using data from 142 patients, for which both standard MR images and quantitative MRF maps of the knee were available. The study demonstrates that synthesizing contrast-weighted images from MRF-parameter maps is possible utilizing DCNNs. Furthermore, the study indicates a need to tune up the dictionary used in MRF so that the parameters expected from the target anatomy are well-covered.
This abstract and the presentation materials are available to members only;
a login is required.