Abstract #0097

Deep-Learning-based contrast synthesis from MRF parameter maps in the knee

Olli Nykänen^1,2, Antti Isosalo¹, Satu I Inkinen¹, Victor Casula^1,3, Mika Nevalainen^1,3,4, Riccardo Lattanzi⁵, Martijn Cloos⁶, Mikko J Nissi², and Miika T Nieminen^1,3,4

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

Synopsis

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.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.