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

Nykänen, Olli; Isosalo, Antti; Inkinen, Satu; Casula, Victor; Nevalainen, Mika; Lattanzi, Riccardo; Cloos, Martijn; Nissi, Mikko; Nieminen, Miika T. (2022-05-12)
 
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https://archive.ismrm.org/2022/0097.html

Nykänen, Olli
Isosalo, Antti
Inkinen, Satu
Casula, Victor
Nevalainen, Mika
Lattanzi, Riccardo
Cloos, Martijn
Nissi, Mikko
Nieminen, Miika T.
Society of Magnetic Resonance
12.05.2022

Nykänen, Olli; Isosalo, Antti; Inkinen, Satu; Casula, Victor; Nevalainen, Mika; et al. (2022) Deep-Learning-based contrast synthesis from MRF parameter maps in the knee. Published in Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting (ISMRM 2022), 07-12 May 2022, London, England, UK, article number 0097, https://archive.ismrm.org/2022/0097.html

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© International Society for Magnetic Resonance in Medicine (ISMRM), Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting (ISMRM 2022). Published in this repository with the kind permission of the publisher.
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https://urn.fi/URN:NBN:fi-fe2023063068891
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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.

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