DeepCEST 3T: Robust neural network prediction of 3T CEST MRI parameters 
including uncertainty quantification

Glang, F; Deshmane, A; Prokudin, S; Martin, F; Herz, K; Lindig, T; Bender, B; Scheffler, K; Zaiss, M

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DeepCEST 3T: Robust neural network prediction of 3T CEST MRI parameters including uncertainty quantification

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Glang, F
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons215996

Deshmane, A
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons182287

Martin, F
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Herz, K
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84187

Scheffler, K
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons214560

Zaiss, M
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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引用

Glang, F., Deshmane, A., Prokudin, S., Martin, F., Herz, K., Lindig, T., Bender, B., Scheffler, K., & Zaiss, M. (2020). DeepCEST 3T: Robust neural network prediction of 3T CEST MRI parameters including uncertainty quantification. Poster presented at 2020 ISMRM & SMRT Virtual Conference & Exhibition.

引用: https://hdl.handle.net/21.11116/0000-0006-D869-1

要旨

Analysis of CEST data often requires complex mathematical modeling before contrast generation, which can be error prone and time-consuming. Here, a probabilistic deep learning approach is introduced to shortcut conventional Lorentzian fitting analysis of 3T in-vivo CEST data by learning from previously evaluated data. It is demonstrated that the trained networks generalize to data of a healthy subject and a brain tumor patient, providing CEST contrasts in a fraction of the conventional evaluation time. Additionally, the probabilistic network architecture enables uncertainty quantification, indicating if predictions are trustworthy, which is assessed by perturbation analysis.